{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 支持向量机（SVM）\n",
    "\n",
    "- 与传统算法进行对比，看看SVM究竟能带来什么样的效果\n",
    "- 软间隔的作用，这么复杂的算法肯定会导致过拟合现象，如何来进行解决呢？\n",
    "- 核函数的作用，如果只是做线性分类，好像轮不到SVM登场了，核函数才是它的强大之处！"
   ],
   "id": "69c1969d4f63746f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T08:40:43.892413Z",
     "start_time": "2025-04-10T08:40:42.927753Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "id": "a96c2e03b7882402",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 支持向量机带来的效果",
   "id": "46c92405727630c4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:44:25.117994Z",
     "start_time": "2025-04-10T09:44:25.105374Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, (2, 3)]\n",
    "y = iris.target\n",
    "\n",
    "binary_class = (y == 0) | (y == 1)\n",
    "X = X[binary_class]\n",
    "y = y[binary_class]\n",
    "\n",
    "svm_clf = SVC(kernel='linear', C=float('inf'))\n",
    "svm_clf.fit(X, y)"
   ],
   "id": "307f36d3dfcd3234",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ],
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T10:23:08.952875Z",
     "start_time": "2025-04-10T10:23:08.942615Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_decision_boundary(model, x_min, x_max, sv=True):\n",
    "    w = model.coef_[0]\n",
    "    b = model.intercept_[0]\n",
    "    \n",
    "    x0 = np.linspace(x_min, x_max, 200)\n",
    "    decision_boundary = - w[0] / w[1] * x0 - b / w[1]\n",
    "    margin = 1 / w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "    \n",
    "    if sv:\n",
    "        svc = model.support_vectors_\n",
    "        plt.scatter(svc[:, 0], svc[:, 1], s=200, c='none', edgecolors='k')\n",
    "    \n",
    "    plt.plot(x0, decision_boundary, 'k-', linewidth=2)\n",
    "    plt.plot(x0, gutter_up, 'k--', linewidth=2)\n",
    "    plt.plot(x0, gutter_down, 'k--', linewidth=2)"
   ],
   "id": "c46ed1793c807655",
   "outputs": [],
   "execution_count": 123
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:47:59.150483Z",
     "start_time": "2025-04-10T09:47:59.054963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(svm_clf, 1, 5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "plt.axis((1, 5, 0, 2))"
   ],
   "id": "691b5a8f1814ae3a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(1.0), np.float64(5.0), np.float64(0.0), np.float64(2.0))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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CiKelqsmoifty5LlzrZjds2cP58+ft3im9XExMTFcuHCBsmXLWqXvrVu3cvDgQRls3w516dLF/M3AnTt3GDFihLaBhFVNnjyZVq1a8dNPPzFmzBit4wghhHgKavwPqHdfRI0YlCPPn+ViNiYmhpCQEEJCQgDT8DkhISHmG7bGjBlDr169ntjvs88+w8vLi3r1npwubcSIEezZs4fLly+zf/9+3nzzTfR6PT169MhqPIuGDh1K48aNATh58iSffvqpVZ5XaE9RFJYsWWI+O//555/z22+/aZxKWEvBggXZvXs3HTp00DqKEEKIp6DG/4AaORKM93OsjywXs0ePHqVRo0Y0atQIMI3j2qhRIyZMmADArVu3nhiJIDIykg0bNqR5Vvb69ev06NGDWrVq0bVrV0qUKMHBgwcpVapUVuNZ9Phg+1OnTuXMmTNWeW6hvfLly6f6A6Vfv34ytrAQQgihMVVNRo2ekeP9ZOsGsLwisxcUf/TRR+aip2XLluzZswedzq5m9M23jEYjrVu35vfffwdg5MiRcgbeDqmqyjfffEPRokV5/fXXtY4jhBAiHWriXtSH/45gFRVtoNgzF233BrC8YOLEiVSvXh2AP/74I9VYpcK26XQ6goKCzDcXzp07l+PHj2ucSlhTbGws7du3p2fPnvj6+hIZGal1JCGEEOkx3MuVbvJVMevq6pqqgB01ahQ3btzQMJGwplq1apkvdzEajfTt21cG27cjrq6uKIoCmMasHj16tMaJhBBCpEtfOle6yVfFLMBLL73E//3f/wEy2L49GjlyJPXr1wcgJCSE+fPna5xIWIuiKCxfvpyCBU3ziAcGBpovKxFCCJEHOT0HupI53k2+K2YBZs+eTZkyZVAUhYoVK5KSkqJ1JGElTk5OBAcHm8/gTZw4Mc3xioXtqVSpEjNm/Hszga+vLwkJCRomEkIIkRZFcUApMgFQ/nnkjHxZzBYvXpzVq1fz+++/s2TJEhwdHbWOJKyoWbNmDBkyBICEhAT8/Pzk7LsdGTBgAM899xwAZ8+e5ZNPPtE4kRBCiLQoLq+hFF0MOuvMHWCxj/w0moHIP2JiYqhXrx5XrlwBTOMcP7q8RNi+U6dO0ahRI5KTk3FwcOD48ePmy0uEEELkPapqJOr+XoqWaiOjGeQko9GodQRhJYUKFSIwMNC8PHz4cMLDwzVMJKypbt265hnBUlJS8PHxwWAwaJxKCCFEWhRFh+LUOEeeW4pZwGAwsHjxYpo0aUJ8fLzWcYSVvPbaa7z33nsAREREMHjwYI0TCWv6+OOPqV27NgCHDx9m2bJlGicSQgihBSlmMU13O3jwYEJCQpg8ebLWcYQVzZ8/n5IlTXdSrl+/nh9++EHjRMJanJ2dCQoKAqBHjx5069ZN40RCCCG0INfMAmFhYTRs2JCkpCT0ej1HjhwxT9crbN9XX31lPkNbrlw5Tp8+jZubm8aphLWcOnWKunXrah1DCCFEBnLqHic5MwvUqVOHcePGAaZLDnx8fGS4Ljvy7rvv8tprrwGmwfYfXWsp7IMUskIIkb9JMfuPjz76yPxL8fjx4yxYsEDbQMJqFEUhMDDQPNj+8uXL+eOPPzROJXLKvXv3uH//vtYxhBB2Sk3cg/HBBxjD62MMb4DxgR9q4gG77zsvk2L2H48Ptj9hwgQuXLigcSphLZUrV041HqkMtm9/VFXl66+/pk6dOgwdOlTrOEIIO6TGLEZ96AtJh4BEIAGSfkd92Bs19nO77Tuvk2L2P5577jk+/PBDAOLj4+nXr58Mtm9HBg0aRLNmzQA4c+YM06dP1ziRsKaHDx8yaNAg7t27x//+9z+2bdumdSQhhB1Rk46gxiz+Z+m/QwGa/l+NnoGafNru+rYFUsw+Ztq0aVSqVAmAHTt2sGbNGo0TCWvR6/UEBwfj4OAAwIwZMwgNDdU4lbCW4sWLM3fuXPNy//79iYmJ0TCREMKeqLFfAvp0WuhR476yu75tgRSzjylcuHCqwfbnz58vkynYkfr16/PRRx8BpsH2fX19ZbB9O/LBBx/w0ksvAXDlyhXGjx+vcSIhhN1IPkHqs6KPM0DSCfvr2wZIMWtBu3bt6NmzJwMHDuT3339Hp5OXyZ6MGzeOZ555BoBDhw7JYPt2RFEUVqxYgYuLCwALFy7k0KFDGqcSQtgFxck6bWytbxsgVVoavvjiC5YsWWLVcdBE3uDi4mIebB9gzJgxXL16VcNEwppq1KhhnvxEVVV8fX1JSkrSOJUQwuY5v0T6X/XrwLmN/fVtA6SYTYOcjbVvL774Iv369QMgNjYWf39/udnPjgQEBJgnPgkNDWX27NkaJxJC2DrFtSemskmxsFUHOKO4dre7vm2BVGyZdPXq1VRn84TtmzVrFmXLlgVgy5YtrF27VuNEwlocHBwIDg5GrzedyZgyZQpnzpzROJUQwpYpDlVQii0FnEhdPimguKAUW4miL2N3fdsCKWYzISgoiLp169KvXz/27dundRxhJW5ubqmulx08eDD37t3TMJGwpsaNGxMQEABAwYIFuXz5sraBhBA2T3FujVJqN0qhoeD0Aji9iFJ4FEqpXSjOXnbbd16nqHbw3WpOzfX7yPz5882/FOvUqcOJEydwdna2ej9CG2+//TYbNmwAoFevXjIcmx2Ji4vj448/ZsyYMZQpk3/PWgghRF6QU/WaFLOZYDAYaN68OUeOHAFg4sSJTJo0yer9CG3cunWLOnXqEBkZCcAvv/zCq6++qnEqIYQQwr7kVL0mlxlkgl6vJygoyDzY/vTp0zl16pTGqYS1lC1bljlz5piX+/XrR2xsrIaJhBBCCJFZUsxmkqenJ6NGjQIgOTkZHx8fGWzfjvTt25fWrVsDcPnyZSZMmKBtIJEj7t27R69evfjuu++0jiKEEMJK5DKDLEhISMDT05O///4bgMWLFzNo0KAc60/krnPnzlG/fn0SExPR6XQcPHiQZ599VutYwkquX79Oo0aNuHfvHu7u7pw+fZpixYppHUsIIfINucwgD3BxcWHlypXm5TFjxnDt2jUNEwlrqlmzpvlaaKPRiI+PD8nJydqGElZTvnx5mjdvDkB4eLj5mxYhhDbUlOuoMcsxRk1FjVmJagjXOlKmGJPvYbzzCsbwOqbHndcxJkdmev/sHLeqpqAm/IoxajrGqBmoCbtQ1dz5lthoTMAYNRvjvc4Y772JMXohRmPemJAmy8Xs3r176dixI+XKlUNRFDZt2pRu+927d6MoyhOP8PDUb97SpUupUqUKLi4ueHl5cfjw4axGyxWtWrXCz88PgJiYGBls384MHz4cT09PAP76669U19IK26YoCsuWLaNw4cIABAcHs2vXLo1TCZH/qKoRY9R01Hsvo8YshLhvUGPmod5tjTF6QZ7+nWp8OBzutwDjFcBgehjPw/1nMUZOTXff7B63mhyGevcl1IhBEPc/iPsSNaIf6r22qCkXrXeQFhjjt8CdhhAXBCmnIeUUxC6FOw0xJuzJ0b4zI8vFbGxsLJ6enixdujRL+509e5Zbt26ZH6VLlzZvW7duHQEBAUycOJHjx4/j6elJ27ZtuXPnTlbj5YpHg+1XqFCBfv36oSiWZuQQtsjR0ZHg4GDzDHCTJ082X1YibF+FChWYOXOmednPz4/4+HgNEwmR/6gxiyFuNaACRiDln/8aIXYZxH2uZbw0GWPXQeJPaTeI/xJj3N40N2fnuFXDXdQHvcB49581Kf88AMMN1AfvoxqjsnI4mWZMPgORw/7J+rgUiOiHMeVGjvSdWVkuZtu1a8e0adN48803s7Rf6dKlcXd3Nz/+O13svHnz8PX1pU+fPnh4eBAYGIirqyurVq3KarxcUbRoUTZv3sypU6fo2LGj1nGElTVt2pRhw4YBkJiYiJ+fH0ajpX/Ewhb179+fFi1aAHD+/HmmTJmicSIh8g/VGAOxn6XfJmY5qpo3vr5OJToTPyuihlhcnd3jVuO+ATUa09ngxxnAeA/iN2Sc72lETcVUgKfFCNHTcqbvTMq1a2YbNmxI2bJleeWVV1LNopWUlMSxY8fw9vb+N5ROh7e3NwcOHLD4XImJiURFRaV65LbGjRvn6M1mQluTJ0+matWqAOzZs4fPPkv/h5CwHTqdjqCgIJycnACYPXs2ISEh2oYSIr9I2gckpN9GjYSkI7kSJ2sycw9FGsM6Zve4E37G8plR886oCT9nIt9TSA7JuE3i/pzpO5NyvJgtW7YsgYGBbNiwgQ0bNlCxYkVat27N8ePHAdNQOQaD4YnZecqUKfPEdbWPzJgxAzc3N/OjYsWKOX0YGVJVldu3b2sdQ1hJwYIFWbFihXl55MiR3Lx5U8NEwpo8PDwYO3YsYJoUxcfHh5SUFI1TCZEPqJkcwzuz7WxFdo87M/sbc+o1y8wNZtr+/MzxYrZWrVr069ePJk2a0KJFC1atWkWLFi2YP3/+Uz/nmDFjiIyMND+0HlHg6tWrdOjQgebNmxMXF6dpFmE9r7zyCr179wYgMjKSDz/8UONEwppGjx5N3bp1ATh27JiMPStEbtBXz1w7h2o5myO3Zfe4HWoC+vQ6AIdnspoqc5TCGbfRlciZvjNJk6G5mjVrxvnz5wEoWbIker3+ibOat2/fxt3d3eL+zs7OFClSJNVDS/7+/mzZsoVLly4xceJETbMI65o7dy6lSpUC4Pvvv+f777/XOJGwFicnJ4KCgihZsiRr1qyhW7duWkcSwv45NvinMEur/NCDY2MUhxq5mSpzdFUybqNvZHl9No9bcX2X9M+QGlBce2Sc72kUyMTPRtcPcqbvTNKkmA0JCaFs2bKA6RdKkyZN2LFjh3m70Whkx44d5jEh87p58+bh7Oxs/v9jx45pnEhYS4kSJVi0aJF5edCgQURERGgXSFhV8+bNuXLlCr169ZJRSYTIBYqioLjNApx58kyjHhRXFDdtbyZKU7FNGTRQoOj/LG/J7nE7vwwuHSz3CVCgOzh5ZZDvKRUaCrpyaW/XV7e9YjYmJoaQkBDzDROXLl0iJCSEq1evAqZLAHr16mVuv2DBAn744QfOnz/PyZMnGTp0KDt37mTgwIHmNgEBAQQFBbFmzRrCwsLw9/cnNjaWPn36ZPPwcketWrXM05/KYPv2p1u3brRv3x6AW7du8dFHH2mcSFiTq6ur1hGEyFcUx3ooJb4DZ2/+LUMcwOV1lBIb8uZZWUDn6AoljgAFn9yoFIESJ9E5Oqa5f3aOW1F0KG6zUQqPAV3ZfzfoK6AUmYRSZHKO/UGu0zlAyV9NBXWqslEPLu2hxM+pRqjShJpFu3btUjGN0ZDq0bt3b1VVVbV3795qq1atzO1nzZqlVq9eXXVxcVGLFy+utm7dWt25c+cTz7t48WK1UqVKqpOTk9qsWTP14MGDmc4UGRmpAmpkZGRWD8dqkpKS1Pr165tfj5kzZ2qWRVjflStX1EKFCpnf3927d2sdSeSQX375Rb19+7bWMYTIF4yGaNWYfE01GmK0jpIlhqRY1RC9SjVEf64akpKyvH92jttoNKjGlFumh9GY5f2zw2BIVg2Jf6qGxL9Ug8GQ5f1zql5TVDUPT7WRSTk1129WHT58mObNm2M0GnFxceGvv/6iZs2amuUR1rVkyRLzTWA1a9bkr7/+wsXFReNUwloePHjA0KFD+fLLL+nRowdff/211pGEEMKu5FS9pvF5YfvSrFkzhgwxDZickJCAn59fnp6WT2SNv7+/+Truc+fOMXVq+lMXCttiMBjYsmULAN988w0//5xDYzYKIYSwKilmrWzq1KlUqVIFgN27d+fZWcxE1un1eoKCgnD855qoTz/9lD///FPjVMJaSpUqlWrIQH9/f6KjozVMJIQQIjOkmLWyxwfbP3IkL85iIp5W3bp1+fjjjwFISUnBx8cHgyEzA0oLW/Dee+/xyiuvAHDt2jXzxApCCCHyLrlmNod89NFHtGzZko4dO2odRVhZYmIijRo1IiwsDDANxzZs2DCNUwlruXTpEvXq1SMuLg5FUdi3b5/NDBMo8idVTQbDNUABfSUUJb3B9cUjxuRzkHIJHGugy+VJGlTDfTDeB31JFF3xLO1rNMaZpr1VdODohU7nlGt9Z1dO1WtSzArxFPbv30/Lli1RVRVXV1dOnjxJ1apVtY4lrGTevHkMHz4cME19e+LECZycsvYLQ4icpqrJEBuEGvsFqA9MK3WlUAr+H7h+IEVtGozxP0DUJ6BG/LtSKQFFJqMr8GqO9q0mn0SNngdJ+zANjqOAcxuUQgEojunP4GU0xkGEPyQd/Gdf/tm/FbgtybCozU7f1iI3gAmRh7Ro0YIBAwYAEBcXR//+/eVmPzsyePBgmjZtCsDp06eZOXOmxomESE1VDagRg1FjFv5byAIY76JGz0KNHC0/kywwxn4NkSNTF7IA6n2IHIQxfnOO9a0mHUO93x2S9vNvMapC4h7U+++gJp9Oc1+jMQnuekPSgf/s+2j/3XDvNYxGY470bQukmM0FqqryzTffMG7cOK2jCCuaPn06FSpUAODXX3/lf/+zPPOLsD0ODg4EBwej15vObE2bNo3Tp237h72wM4m/QuIOUhc2/5Hwwz9n4MQjRqMRojOYXSwyZ66TV1UVNXIMkAI8XnQagETUyAlpP0HMHFDvpb3deB3iVuZM3zZAitlc0KNHD959910++eQT/vjjD63jCCspUqQIy5cvNy8PHTqUO3fuaJhIWJOnpyejRo0CoFWrVjJTmMhT1NivSf9XuB417pvcimMb4tdiKujSbYQx/lfr9518HAyXebKYfMQIKX+hJv+dRqwNGfcR+2XO9G0DpJjNBS1atDD/v6+vLwkJCRqmEdbUoUMHunbtCpgG3ZcbwezL+PHjWbt2Lb/++qt5yD0h8gTDJdIuTgAMkHIxt9LYhuSTmWyXA0MuplzJXDvDZcvr1diM93380glr9W0DpJjNBQMHDsTLywuAM2fOMH36dI0TCWtatGgRxYoVA+Drr79m69atGicS1lKgQAG6deuWY3OeC/HUlMIZNQBdRm3yGV0x67bLUt+FMtdOSatdZm7mc8yhvvM+KWZzgV6vJzg4GAcHBwBmzJhBaGioxqmEtZQpU4a5c+eal/v3709MTIyGiUROio+PlxtrhOaUAp3I6Fe44iJDQ6ZS8P8y0UgB1x7W79upJSgFMui6GDg1TWP/ZzPuw/nFnOnbBkgxm0vq1avHmDFjANNg+76+vjLYvh354IMPeOmllwC4evWq3Oxnp3755Rc8PDxYv3691lFEfufa7Z8ziJbO2OlB5w4F3sztVHmaTl/CVNilx/k1dLqCVu9b0bmiFPRPv02hD1GUNIbXKjIeSO8bIh0UtnzzWrb7tgEyzmwuSkxMpGHDhpw5cwaAhQsXMnjwYI1TCWu5cOEC9erVIyEhAUVROHDggPnyEmH7Dh8+bH4/S5cuTVhYGMWL5+6A40L8l5pyAfWh/z/XOjpgGtnAAA7PoBRdjuJQUduAeZDRaISHPSH52JMbnVpC0WB0upw5z6eqqmkotdgVmK531mMaTUCPUmgIFPRL95ImY8JuiBjAkzexOUGxVeicm+VY39Yikyakw1aKWYA//viDF154ATBNfXvq1CkqV66scSphLbNnzzbfAV+vXj2OHTsmg+3bCVVV6dKlC5s2bQKgT58+rFq1SttQIt9TVSMkHUBNOgIoKE7PgVMzuc47A8bkvyFmIRjvgq4sFB6GzqFKrvStGu5Bws+oxrsoOnco0B4lk9fpGo0pELcKEndhmvTgVXDtlekCPDt9W4MUs+mwpWIWwN/fn8DAQAC6devG2rVrNU4krCUlJYVmzZpx4sQJAKZOnSqXHNiRGzdu4OHhQVRUFADbt2/H29tb41RCCGEbpJhNh60Vs5GRkXh4ePDCCy+wcOFCypQpo3UkYUXHjx+nWbNmGAwGnJyc+PPPP6ldu7bWsYSVrFixgv79+wNQrVo1QkNDZQxaIYTIBJnO1o64ubkREhLC2rVrpZC1Q40bN2b48OEAJCUl4evrm+40g8K2+Pr6mi8VunjxIpMmTdI2kBBC5HNyZlaIHBAXF0eDBg24cOECAMuXLzefzRO27+zZszRo0ICkpCR0Oh1HjhyhcePGWscSQog8Tc7M2rn79++br7MUts/V1ZWVK/+dJ3vUqFHcuHFDw0TCmmrVqsWECaa5zI1GI3379iU5OVnjVEIIkT/JmVmNqarK2rVrGTJkCK6urpw8eZJChWx3Fg6RWt++fc13vHfq1IlNmzbJXcZ2IikpiaZNmxIaGkqBAgXYs2cPzz6biYHNRZ6jqimQuAs1fiMY74CuHIprF3B6EUWx33M+RkMMRI6GpN1AMuAELq9C4Rno9BmPwmJMOgHRMyH5b9MKp7pQaAw6p7oZ72tMgJglEL8R1BjQFQHXruDqj07nkKN9Z/f9Vg03UePWQtJhQEFxbgEFuqLoM75sML9+1h6RG8DSYevFrLe3Nzt37gRg2LBhzJs3T+NUwloePHiAh4cHt2/fBmD9+vW8/fbbGqcS1nL48GEmTJjAsmXLqFatmtZxxFNQjbGoD/0g+QimLyv/Mwan04soxZaiKM7ahswBxuQrcL8tpuN9nAOU2oNOXyrt/aNmQdxnljcWGoauUNqD9BtT7pr6Vi3MlKgUg1Lb0enS/l2enb6z+36rCb+gRgz7Z79Hr50OcEQptgzF+YUc69seSDGbDlsuZgHOnz9P/fr1SUhIQKfTceDAAZo1S3vwY2Fb1q9fT9euXQHT1LdhYWEUK5Z74/oJIdJmjBgBCZuxXNTpoMC76Nwm5HasHGcM9wTi02nhhs79iOV9E3ZCRAb3ABRfh86pkeX977QBYzqXXemfQVdqc870nY33W005j3qvE6bJBh4vnRTACaXULyj6clbv217INbN2rEaNGkyePBkwXX/n4+Mj19/ZkbfffptOnToBcPv2bUaOHKlxIiEEgGq4nU5xgWl9/LeoxsjcjJXjjAm/k34hCxCJMem05U0xczPuJHqG5b6TTqVfyAIY/saYcs3qfWf3/Vbj/oepiLV0DlAFklHjvsmRvkX6pJjNIwICAmjUyPSXZGhoKLNnz9Y4kbAWRVFYunQphQsXBuCzzz4zX1Yi7EtMTAyjR48mPDxc6ygiM5IOk3ZxYW4EScdzI03uiU3jK/on2gVbXp9yMeN9k8Msr4//LnN9x2+0ft/Zfb8T9mA6K5sWIyTuyZm+RbqkmM0jHBwcCA4ORq/XAzBlyhTOnj2rcSphLRUqVGDWrFnmZT8/P+LjMzozImzJiRMnqFevHrNmzWLw4MFaxxGZkl5h8jTtbEVmjyetbwgzc3ViWm1SNOw7u+93ZvbPzr5ZaSf+S4rZPKRx48YEBAQAkJiYiJ+fnwy2b0f69evH888/D8CFCxfMl5YI+1CxYkViY2MB03XSP/zwg8aJRIYcPTPRSAeO9XI8Sq4q0CmT7d6yvF7nnvG++sqW17u8mrm+ndNol52+s/t+OzXFdMNWmh2DYxojmuTXz1oukWI2j5k0aZL5rui9e/cSHJzG1zzC5uh0OoKCgnByMg15M2fOHBlb2I6ULFmSBQsWmJcHDBhAZKRc/5aXKQ5VwakFaRcoenB+FUWfiQLKhuhc3yH9ogzAGZ1La8ubCqY9WoBZoSGW+3Z+ARS39PfVlUHnVN/qfWf3/VZc3yejywwU13dzpG+RviwXs3v37qVjx46UK1cORVHYtGlTuu2///57XnnlFUqVKkWRIkVo3rw5v/zyS6o2kyZNQlGUVI/8Opf9fwfbL1asmIw5a2fq1KnDuHHjADAYDPj4+JCSktmv3URe9+677/Laa68BcPPmTcaMGaNxIpERxW0W6MtiuhvdvNb00FdBcbPTb1CKrkx/e/Gv0tykK9gNnFunva9LR3QF0jkDW2w1aRd1jlD8yxzrOzvvt+LUCKXwR/8s/Te/HlBQikxGcXwmR/oW6ctyMRsbG4unpydLly7NVPu9e/fyyiuvsGXLFo4dO0abNm3o2LHjE2ek6taty61bt8yPP/74I6vR7MbLL7/MihUrCAsL4913Lf+VJ2zXRx99RN26poG9jx8/nupsnrBtiqIQGBhIwYIFAdM0xvn5Z5ktUPRlUEpsQik0HPRVQCkE+qoohUejlPgORWefw+jpXF6A4j+Zjvm/9DWhxHZ0Tg3S37/YSig8DpRHY9EqoCsLRaahK5r+iAM6p7pQ8jdwegF4NEGCIzi/DCV3o3Ooks7e2es7u++3UrAvSvGvwPkl0xlmpSg4t0Upvg7FtXuO9i3Slq1xZhVFYePGjXTu3DlL+9WtW5du3bqZp4OcNGkSmzZtIiQk5Kly2Po4syL/OXjwIC1atEBVVQoUKEBoaCjVq1fXOpawkoULFzJ06FAAateuTUhICM7O9j0YuhBCZMRuxpk1Go1ER0dTvHjxVOvPnTtHuXLlqFatGj179uTq1au5HS3PMxjkLkd78dxzz/Hhhx8CEB8fT79+/bCD+UvEPwYNGmSe+OTMmTNMnz5d40RCCGG/cr2YnTNnDjExMeYZkQC8vLxYvXo127ZtY/ny5Vy6dIkXXniB6Ohoi8+RmJhIVFRUqoc9u3//Pr1798bfPxMXvgubMW3aNCpVqgTAjh07WLNmjcaJhLXo9XqCg4NxcDB9hTpjxgzOnDmjcSohhLBPuVrMfv3110yePJlvv/2W0qVLm9e3a9eOd955hwYNGtC2bVu2bNlCREQE3377rcXnmTFjBm5ubuZHxYoVc+sQcl1KSgpeXl588cUXBAUFsXv3bq0jCSspXLgwgYGB5uWAgABu376tYSJhTfXr1+ejjz6iQIECzJw5kxo1amgdSQgh7FKuFbNr167Fx8eHb7/9Fm9v73TbFi1alGeeeYbz589b3D5mzBgiIyPNj2vX0pj2zg44ODiYx54FGWzf3rRr1858k9/Dhw8ZMsTykDLCNo0bN45Tp04REBBgPksrhBDCunKlmP3mm2/o06cP33zzDe3bt8+wfUxMDBcuXKBs2bIWtzs7O1OkSJFUD3vWv39/WrRoAZiuLZ46darGiYQ1LViwgBIlSgCwbt06fvrpJ40TCWtxcXGhatWqWscQdk5NuYYa/yNq/E+ohltZ21dNRk3cixr/PWriPlQ19+7NyK99Z1d23m97leXRDGJiYsxnTBs1asS8efNo06YNxYsXp1KlSowZM4YbN27wxRdfAKZLC3r37s3ChQvp0qWL+XkKFCiAm5tp4OQRI0bQsWNHKleuzM2bN5k4cSIhISGcPn2aUqVKPRniMflhNIPTp0/TqFEjkpKS0Ov1HDt2DE/PzMwoImzBl19+Sa9evQDT1LenTp2y289yfhcWFkbt2rVRFCXjxkKkQzU+QI38GBJ38e8Urgo4v4biNg1FVzj9/eN/QI2eCcb7/67UlUYpMh7FpW2O5c7PfWdHdt/vvCDPjGZw9OhRGjVqRKNGjQDTdX6NGjUyD7N169atVCMRrFy5kpSUFAYOHEjZsmXNj/9+nXr9+nV69OhBrVq16Nq1KyVKlODgwYOZKmTzCw8PD8aOHQvIYPv26L333uPVV00DfV+/fp2PP/5Y40TC2mJjYwkICKBevXp88803WscRNk41xqE+eA8S9/BvYYPp/xN/QX3wAaqalPb+8T+gRo5MXdABGO+iRgxGTdieI7nzc9/Zkd33295la5zZvCI/nJkFSEpKolGjRpw+fRqAuXPnprqeVti2S5cuUa9ePeLi4lAUhd9//53nn39e61jCSn7++Wc6dOgAmKa+DQsLo2TJkhqnErZKjfsKNWoKqQub1BS3OSgFOj25r5qMevcFMD5Ia0/Ql0cp+RuKYt2rEfNr39mVnfc7L8kzZ2aFdpycnAgODjZ/PTlu3DguXryocSphLVWrVmXatGkAqKqKr68viYmJGqcS1tK+fXvefvttAO7du8fw4cM1TiRsmRr3XQYtdKjxabRJOpBOQQegguE6JIc8Zbp05Ne+sylb73c+IMWsjWnevDkDBw4ETIPtf/bZZxonEtY0ePBgmjZtCpiurZwxY4bGiYQ1LV68mKJFiwLwxRdf8Ouvv2obSNgu413SO0sHRjDcsbzJcC8LfVhZfu07u7LzfucDUszaoOnTp+Pp6cnnn39uPpMn7MOjwfb1ej1geq8fXVYibJ+7uztz5swxL/fr14/Y2FgNEwmbpSsDpHcToQ70lkcEQl/a8vonnsI9q6kyll/7zq7svN/5gBSzNqhw4cIcP36cDz74QO6ItkOenp6MGjUKgOTkZHx8fDAajRqnEtbyf//3f7Rp0waAy5cvm2+eFSIrFNd3yOhMnVLgHcubnJ4DXXrXayugrwyODbKRMA35te9sytb7nQ9IMWujdDp56+zZ+PHjqVmzJgAHDhxg+fLlGicS1qIoCitWrMDFxQUwjTN85MgRjVMJm1OgMzh4AHoLG3Xg2ARcXrG4q6I4oBSZgOlM3+MnREzrlCLjc+RkSX7tO9uy8X7nB1IR2Ylff/2VnTt3ah1DWEmBAgUICgoyL48ePdquZ7rLb2rWrMmkSZMAMBqN+Pj4kJycrG0oYVMUxQWl+Bfg0o7Uv8odwKULSrHPUBTHtPd3eQ2l6GLQPfbVtL4iSrGVKM4v5kju/Nx3dmT3/bZ3MjSXjYuPj6d///588cUXVKxYkVOnTlG4cN4fOFlkjp+fn7mo7dChAz/++GPePGsgsiw5OZlmzZoREhJCkyZN+OGHHyhXrhzHjh3j4MGDnDx5kri4OAoVKkT9+vVp3rw5np6e8v6LJ6iGu5D8J6CAUyMUXfHM76saIfkEGO+Zrst0zL3PWH7tO7uy835rLafqNSlmbZyqqrz66qv89ttvgOlu+IULF2qcSlhLREQEderUITw8HIC1a9fSrVs3jVMJazl27Bh79uxh0KBBfPXVV8ydO5dTp07h5OREnTp1KFKkCBEREYSFhZGSkkKjRo0YMWIEPXr0sJlfvEII8YiMMyssenT9XYECBQDT0D8HDx7UOJWwlqJFi7J06VLz8ocffsj9+/fT2UPYkiZNmtC1a1fatWvH//3f/1GzZk22b99OdHQ0ISEh7N27l7/++ovo6Gh+/vln3N3d6dmzJx07duTOnfw7DI8QQvyXFLN2oFq1akydOhUwnan18fEhKSn/Tmtnb7p06cKbb74JwN27dxkxYoTGiYS1XLp0iRYtWvD333+zfft2Nm7ciLe3N05OTqnaubi48Prrr7NlyxZ+/PFHjh49SsuWLbl586ZGyYUQIu+QYtZODBkyhCZNmgBw6tQpZs2apXEiYU1LliwxfyWzevVq82UlwnbFx8fz+uuv4+TkxMGDB/H29ubw4cO0atWKW7dupblfx44d2b9/P/Hx8XTq1EluHBNC5HtSzNoJBweHVIPtT5s2jbCwMI1TCWspV64cs2fPNi/7+fkRFxenYSKRXePHj+fSpUv89NNPlC9fnnXr1tG8eXP27t3LoEGD0t23WrVqbNy4kZCQEGbOnJlLiYUQIm+SYtaONGzYkJEjRwKQlJSEr6+vDLZvR3x8fHjxRdOwMZcuXWLixIkaJxJPKzw8nEWLFjFu3Djq1KkDgLe3NyVLmgZ0//777/nu8zoYwz0w3mmFGrMU1RiZ6jmaNm1KQEAAs2bNIioqKtePQViHanyAMXoBxjst032/09w/cQ/GBx9gDK+PMbwBxgd+qIkHcji1EHmLFLN2ZsKECdSoUQOAffv2sWLFCo0TCWvR6XSsXLkSZ2dnAObNm8exY8c0TiWexqpVq3BwcGDgwIHmdSVKlGDhgknm5cFjzhERmQjGW6gxi1HvdzENyfMfQ4YMISEhgS+//DK3ogsrUg03UO+9AbGBYLwDpKT7fj+xf8xi1Ie+kHQISAQSIOl31Ie9UWM/z41DECJPkGLWzhQoUICVK1cCpjM9bdu21TiRsKZatWqZpz+VwfZt1/bt23nttdcoVqxYqvXvvPI77V8pCMCt2wZGT7v3zxYjGG6iRo1P1b58+fK0bt2a7du350ZsYWVqxCjTOKc8/g2a5fc71b5JR1BjFv+zZPjPFtP/q9EzUJNPWzOuEHmWFLN2qE2bNvz+++/8+uuvVKtWTes4wspGjhxJ/fr1AQgJCWHevHkaJxJZoaoqx48f59lnn029PuU8SspRls4oRaGCpjFkg/4XxZ79j66NNkDiLlRD6hEMmjZtKmfobZCach6Sj5C6EP0vy++3ef/YL7E8tekjetS4r7KZUgjbIMWsnWrZsqUMqm6nHB0dCQ4ORqcz/fOdNGkS586d0ziVyKzExESioqKoWLFi6g3JoQBULO/IjLElzav7jbxDQsKjM3cqJJ9KtVvFihW5fft2TkYWOeGf9zt9T77f/+5/grQLYUzbkk48RTAhbI8Us/lEbGwskZGZu6FA5H3NmjVjyJAhACQkJODn54cdTOaXLzz6I9NgeLwQ+Xde9f693Wje1AWAcxeTmTb/wX+eIPX86waDwTyKibAljhk3gSfe73/XO1len9U2QtgBKWbzgV9//ZV69eoREBCgdRRhRVOnTqVKlSoA7N69m1WrVmkbSGSKs7MzZcuW5e+//069wek5Hn1trNMprJhTGsd/6pj5KyO4ey8FcAHHpql2+/vvv6latWrOBxfW9Z/3O21Pvt9mzi9lsL8OnNs8XTYhbIwUs3YuIiKCd955h8uXL7Nq1Sp27NihdSRhJQULFiQwMNC8PGLEiHQH2xd5R5MmTdi/f3+qdYq+JBR4k0c/luvWcmbM4OI0aeDM/s0VKVXSEVzfRdEVSrXf/v37zROmCNvx+PttoYXF99u81bXnP/taupxMBzijuHa3SlYh8jopZu1c0aJFU80G1q9fPxls3460bduW999/HzD94TJ48GCNE4nM6NKlC7t37+bChQup1itFJoDT8/8s6RkzuDj7f66CZ11ncH4VpfDwVO1DQkI4ceKEebpjYVsef79T/dfC+51qX4cqKMWWAk6k/lWugOKCUmwlir6M9UMLkQcpqh1caBcVFYWbmxuRkZHmKT/Fv4xGI61ateKPP/4AYNSoUTLdrR25d+8ederU4d490zBOGzdupHPnztqGEumKj4+nQoUKdOjQgTVr1qTapqoqJO1Hjd9kGntUXxalwFvg2DTVTZ2qqtKlSxeOHj3KpUuXcHBwyOWjENaQ2fc7zf0N9yF+PWrSEUBBcW4OBbqg6IpluK8QuS2n6jUpZvOJM2fO4OnpSVJSEnq9nsOHD9O4cWOtYwkr+frrr+nZsydgmvr29OnTuLm5aZxKpGfVqlX07duXTZs28cYbb2Rqn6SkJL799lt69uzJV199xfvvv893333HW2+9lcNphRAi+3KqXpPLDPKJ2rVrM368aQBug8GAj48PKSkpGqcS1tKjRw/atWsHwM2bNxk9erTGiURG+vTpQ+fOnXn33XfZtWtXhu2PHj1K06ZNef/99xkxYgR9+/alV69eUsgKIfI9KWbzkVGjRlGvXj0ATpw4wfz58zVOJKxFURSWL19OwYKm2aMCAwP5/fffNU4l0qMoCl999RUtW7bklVdeYcKECSQmJqbZPjw8nNBQ09ik8+bNw9vbm6CgoNyKK4QQeZYUs/mIk5MTwcHB5uuwJkyYwPnz5zVOJaylcuXKTJ8+3bzs6+tLQkKCholERlxdXdm8eTPjx49nxowZVKlShXHjxrFnzx4iIiIwGo08fPiQnTt3snfvXpyc/h03tGjRoqmWhRAiv5JiNp/x8vIy3/GuKAohISHaBhJWNXDgQLy8vAA4e/Ysn3zyicaJREYcHR2ZOHEioaGhvPXWWyxatIjWrVtTrFgx9Ho9xYsX5+WXXyY4OJgPPvjAfC30119/zdatWzVOL4QQ2pMbwPKhmJgYBgwYwMSJE6levbrWcYSVnTx5kkaNGpGSkoKDgwPHjx+nfv36WscSmZSSkkJYWBgnT54kLi6OQoUKUb9+fWrVqoVer+fzzz/n//7v/wCoVKkSp06dolAhy2OR5jajMQFiFkPSPkAB59ZQ0B+dLufPIGvZt6qmQOJO1KSjgILi9Bw4v4ii5P2Z2dSU65DwE6rxHoquDBTohKJ3z9y+NnzcQht5ZjSDvXv3Mnv2bI4dO8atW7cyNQzQ7t27CQgI4NSpU1SsWJFx48bxwQcfpGqzdOlSZs+eTXh4OJ6enixevJhmzZplKpMUs0KkNmHCBKZOnQqYpr7dv3+/THlqJ1RVxdvbm507dwIwZMgQFixYoG0owBi/BSIDAONjWxyg6HJ0Lq3ssm81OQz1YT8whpv6AyAF9JVMY706VMuxvrNDVY2o0TMhbg2miRd0mF+/gv1RCg1Jd2gwWz1uoa08M5pBbGwsnp6eLF26NFPtL126RPv27WnTpg0hISEMHToUHx8ffvnlF3ObdevWERAQwMSJEzl+/Dienp60bduWO3fuZDWeEAIYO3YstWvXBuDw4cMsWbJE40TCWhRFYeXKlbi4uACwaNEiDh06pGkmY/IZiBzGk8UkQApE9MOYcsPu+lYNd1Ef9ALj3X/7459RYgw3UB+8j2qMypG+s0uNWQxxqwEV02uX8s9/jRC7DOI+T3tfGz5uYZ+yXMy2a9eOadOmZXrGmcDAQKpWrcrcuXOpU6cOgwYN4u233051J/28efPw9fWlT58+eHh4EBgYiKurq8w1n0tiY2MZPnw4P/30k9ZRhJU4OzunutN97NixXL58WbtAwqqqV6/OlClTANOZWh8fH5KSkrQLFDUVU1GUFiNET7O7vtW4b0CNBgwWthrAeA/iN+RI39mhGmMg9rP028QsR1Utf6Zs9biF/crxG8AOHDiAt7d3qnVt27blwIEDgGkQ8GPHjqVqo9Pp8Pb2Nrd5XGJiIlFRUake4uncunWLevXqMW/ePPz9/eW1tCMtW7bE398fMP3B0r9/f+zgEnnxj2HDhtGoUSP0ej0dO3bU9r1NDsm4TeJ+++s74WcsnxF+REVN+Dln+s6OpH1ABiOdqJGQdMTyNls9bmG3cryYDQ8Pp0yZ1PNDlylThqioKOLj47l37x4Gg8Fim/DwcIvPOWPGDNzc3MyPihUr5lh+e+fu7k6tWrUAuHHjBmPGjNE4kbCmGTNmUK5cOQB++eUXvv76a40TCWtxcHBgzZo1HDt2jOnTp+Ps7KxhGktn6B6XU5O0aNi3GptxG2Mm2uS2zOROr52tHrewWzY5NNeYMWOIjIw0P65du6Z1JJulKIr5sg6AZcuWsW/fPo1TCWtxc3Nj2bJl5uWhQ4dy7949DRMJa6pfvz6enp5axwClcMZtdCXsr2+HmkB6N1bqweGZnOk7O/SZHMUmrZu4bPW4hd3K8WLW3d2d27dvp1p3+/ZtihQpQoECBShZsiR6vd5iG3d3y8ODODs7U6RIkVQP8fSqVKmSajxSHx+fdGciErbljTfe4O233wbg3r17BAQEaJxI5KS4uLjc77RAt4zbuH5gd30rru+S/plhA4prjxzpO1scG/xTkKZVAujBsTGKQw2LW232uIXdyvFitnnz5uzYsSPVuu3bt9O8eXPANCtVkyZNUrUxGo3s2LHD3EbkvA8//JBnn30WgDNnzjBjxgyNEwlrWrx4MUWLFgXgyy+/TDWaiLAPSUlJfPLJJ1SpUoUbN3Lm7v00FRoKunJpb9dXz7liVsu+nV8Glw4WNvwzpFWB7uDklTN9Z4OiKChuswBnnjzDqgfFFcUtnZvmbPS4hf3KcjEbExNDSEiIeeaoS5cuERISwtWrVwHTJQC9evUyt+/fvz8XL15k1KhRnDlzhmXLlvHtt98ybNgwc5uAgACCgoJYs2YNYWFh+Pv7ExsbS58+fbJ5eCKz9Ho9wcHBODiYxgucPn06p06d0jiVsBZ3d3fmzJljXu7Xrx8xMTEaJhLWNm3aNMaNG8fdu3cZMGBArt4QptM5QMlfTUVOql8renBpDyV+RqfLmXMnWvatKDoUt9kohceArux/uq6AUmQSSpHJ6Y7VqiXFsR5Kie/A2Zt/XzcHcHkdpcSGNM/Kgm0ft7BTahbt2rVLxTQOSqpH7969VVVV1d69e6utWrV6Yp+GDRuqTk5OarVq1dTPP//8ieddvHixWqlSJdXJyUlt1qyZevDgwUxnioyMVAE1MjIyq4cjHjN27Fjze/rcc8+pKSkpWkcSVmI0GtU2bdqY399hw4ZpHUlY0f3799UyZcqY39/169drksNgSFYNiX+qhsS/VIPBkG/6NhoNqjHllulhNOZq39llNESrxuRrqtEQk/V9bfi4Re7LqXpNprMVqSQkJODp6cnff/8NmL6Sfu+99zROJazl3LlzNGjQgISEBHQ6HQcOHMj0THsi71u/fj1du3YFTCPChIWFUaxYMY1TCSGESZ6ZAUzYNxcXF4KCgnB1dWX27Nl0795d60jCimrWrMmkSZMA07XpPj4+JCcnaxtKWM3bb79Np06dANNNtCNHjtQ4kRBC5Dw5Myssun//PiVK5NBwNkJTycnJNGvWzHzd+yeffMLHH3+sbShhNdevX8fDw4Po6GgAduzYwUsvvaRxKiGEkDOzIpdJIWu/HB0dCQ4ONt8UM2XKFM6ePatxKmEtFSpUYNasWeZlPz8/4uPjNUwkhBA5S4pZkSlHjx6VwfbtSJMmTczjzSYmJuLn54fRmN70lMKW9OvXj+effx6ACxcuMHnyZI0TCSFEzpFiVqQrNjaW4cOH4+XlxfDhw7WOI6xo8uTJVKtmmuFn7969BAcHa5xIWItOpyMoKAgnJycAfvzxR5kIRQhht+SaWZGuW7duUadOHSIjIwH45ZdfePXVVzVOJazlt99+45VXXgGgSJEihIWFUa5cOgPQC5syY8YMoqOjGT9+PAUKFNA6jhAin8upek2KWZGh4OBgfH19AdPUtydPnqRgwYIapxLW0qdPH1avXg3Am2++yffff69tICGEEHZJbgATmunbty+tW7cG4PLly0yYMEHbQMKq5syZQ+nSpQHYuHGjFLNCCCFsihSzIkOKorBy5UqcnZ0BWLBgAUeOHNE4lbCWEiVKsGjRIvPywIEDiYiI0C6QyDFnz57lo48+ytWpboUQIqdJMSsyRQbbt29du3alQ4cOAISHhzNq1CiNEwlrCwwMxNPTk08//ZQ1a9ZoHUcIIaxGilmRacOHD8fT0xOAv/76izlz5micSFiLoigsW7aMQoUKARAUFMTu3bu1DSWsqnLlyuYRDQICArh9+7bGiYQQwjqkmBWZ9vhg+5MnT+bvv//WOJWwlooVKzJz5kzzsgy2b1/atWvHu+++C8DDhw8ZMmSIxomEEMI6pJgVWdK0aVOGDRsGQN26dUlJSdE4kbAmf39/mjdvDsC5c+eYOnWqxomENS1YsMA8u9+6dev46aefNE4khBDZJ0NziSyLjY1lzZo1+Pn54eDgoHUcYWWnT5+mYcOGJCcno9frOXbsmPnyEmH7vvzyS3r16gWYpr49deqU/NwUQuQKGZpL5BkFCxZkwIABUsjaKQ8PD8aOHQuAwWDAx8dHzsDbkffee8888cn169fN77UQQtgqKWaFVUixY19Gjx6Nh4cHAEePHk01dJewbYqiEBgYiKurKwBLly5l//79GqcSQoinJ8WsyLYjR47QuHFjNm7cqHUUYSXOzs4EBwejKAoA48aN4+LFixqnEtZStWpVpk2bBoCqqvj6+mIwGDROJYQQT0eKWZEtJ06c4LnnniM0NFQG27czzZs3Z+DAgQDEx8fTv39/GWzfjgwePJhnn32WmjVrsnTpUvR6vdaRhBDiqUgxK7KlYcOGvPbaawDcunWL0aNHa5xIWNP06dOpUKECANu3b+fLL7/UOJGwFr1ez4YNG/jzzz/N01ULIYQtktEMRLZdvXoVDw8PYmNjAdizZw8vvviixqmEtWzevJmOHTsCULx4ccLCwihdurTGqYQQQtgaGc1A5FmVKlVixowZ5mVfX18SEhI0TCSsqUOHDnTv3h2ABw8eMHToUG0DiRxjNBo5deqU1jGEECJLpJgVVjFgwACee+45AP7++28++eQTjRMJa1q4cCHFixcH4JtvvuHnn3/WOJGwtrNnz9K6dWuee+45rl27pnUcIYTINClmhVXo9XqCg4NxdHQEYObMmYSGhmqcSlhL6dKlmTdvnnnZ39+f6OhoDRMJa1u4cCG///47MTExDBgwQG72E0LYDClmhdXUrVuXMWPGAKZxZ318fGS4HzvSq1cvvL29Abh27ZoMtm9npk+fTtmyZQHTddLffvutxomEECJz5AYwYVWJiYk0bNiQM2fOoNfr2bNnD88//7zWsYSVXLhwgfr16xMfH4+iKOzfv998eYmwfRs3bqRLly4AlCpVirCwMEqUKKFxKiGEvZAbwIRNeDTYftOmTTly5IgUsnamevXqTJkyBTANtu/j40NSUpLGqYS1vPnmm+Zi9u7du4wYMULjREIIkTE5MytyhKqq5tmjhH1JSUnBy8uL48ePAzBlyhTGjx+vcSphLTdv3sTDw4PIyEjANL7wo8tLhBAiO+TMrLApUsjaLwcHB4KDg80zRk2bNo2wsDCNUwlrKVeuHLNnzzYv+/n5ERcXp2EiIYRI31MVs0uXLqVKlSq4uLjg5eXF4cOH02zbunVrFEV54tG+fXtzmw8++OCJ7Y9mlRK2Lzk5mdmzZ3P37l2towgradSokfkr6KSkJHx9fTEajRqnEtbSt29fWrVqBcClS5eYMGGCxomEECJtWS5m161bR0BAABMnTuT48eN4enrStm1b7ty5Y7H9999/z61bt8yPkydPotfreeedd1K1e+2111K1++abb57uiESeEhYWRtOmTRk1ahQBAQFaxxFWNHHiRKpXrw7Avn37WLFihcaJhLXodDpWrlyJs7MzALGxsTJUlxAiz8ryNbNeXl48++yzLFmyBDDNGFOxYkU+/PBDRo8eneH+CxYsYMKECdy6dYuCBQsCpjOzERERbNq0KetHgFwzm5eFh4dTp04dIiIiANi6daucdbcju3bt4qWXXgKgcOHCnD59mgoVKmicSlhLcHAwNWrUoHXr1lpHEULYgTxxzWxSUhLHjh1LdTOATqfD29ubAwcOZOo5PvvsM7p3724uZB/ZvXs3pUuXplatWvj7+3P//v00nyMxMZGoqKhUD5E3ubu7M3fuXPNy//79iYmJ0TCRsKY2bdrQt29fAKKjo2WwfTvj4+MjhawQIs/LUjF77949DAYDZcqUSbW+TJkyhIeHZ7j/4cOHOXnyJD4+PqnWv/baa3zxxRfs2LGDWbNmsWfPHtq1a5fmgPszZszAzc3N/KhYsWJWDkPksj59+pjP3l25ckXufLczs2fPNv9M+Omnn/juu+80TiSEECI/ydXRDD777DPq169Ps2bNUq3v3r07nTp1on79+nTu3JnNmzdz5MgRdu/ebfF5xowZQ2RkpPkh84jnbYqisGLFClxcXADTtJmHDh3SOJWwlmLFipkvOwIYNGgQDx480DCRyAlGo5EVK1bw5ptvytl3IUSekqVitmTJkuj1em7fvp1q/e3bt3F3d09339jYWNauXWv+SjI91apVo2TJkpw/f97idmdnZ4oUKZLqIfK2GjVqMHnyZMA0Bq2vr68Mtm9H3nrrLd544w0A7ty5w8iRIzVOJKytd+/e9O/fn02bNrFq1Sqt4wghhFmWilknJyeaNGnCjh07zOuMRiM7duygefPm6e67fv16EhMTee+99zLs5/r169y/f988T7iwDwEBATRq1AiA0NDQVGNZCtumKApLly41/2G5atWqVD8nhO3778/uESNGcOvWLQ3TCCHEv7J8mUFAQABBQUGsWbOGsLAw/P39iY2NpU+fPgD06tWLMWPGPLHfZ599RufOnZ+Y5zsmJoaRI0dy8OBBLl++zI4dO3jjjTeoUaMGbdu2fcrDEnnR44PtT5kyhbNnz2qcSlhL+fLlmTVrlnm5X79+Mti+HWnbti3vv/8+ABEREQwePFjjREIIYZLlYrZbt27MmTOHCRMm0LBhQ0JCQti2bZv5BpCrV68+8Rf72bNn+eOPPyxeYqDX6/nrr7/o1KkTzzzzDH379qVJkyb8/vvv5jEOhf1o3LgxAQEB6PV6AgICqFSpktaRhBX5+fnRsmVLAC5cuGC+tETYh3nz5lGyZEkAvvvuO3744QeNEwkhxFOMM5sXyTiztiUuLo5z587h6empdRSRA86cOYOnpydJSUno9XoOHz5M48aNtY4lrOTrr7+mZ8+egGnq29OnT+Pm5qZxKiGELcgT48wKYQ2urq5SyNqx2rVrm4dfMxgM+Pj4kJKSonEqYS09evSgXbt2ANy8edPiZWVCCJGbpJgVecKNGzdkuB87MmrUKOrVqwfAiRMnmD9/vsaJhLUoisLy5cvNE98sX76cP/74Q+NUQoj8TIpZoank5GSmT59O9erV+f7777WOI6zEycmJ4OBgFEUBYMKECWkOtSdsT+XKlZk+fbp5ecKECRqmEULkd1LMCk39+uuvjB07lsTERAYNGsTDhw+1jiSsxMvLy3zHe0JCAv369ZOz73Zk4MCBPPfcc/j5+ckfokIITckNYEJTqqrSuXNnfvzxR8A0F3xQUJDGqYS1xMTEULduXa5evQqYxp99NIyfsH2JiYky6owQItPkBjBhlx4Ntl+4cGEAgoOD2bVrl8aphLUUKlSIwMBA8/Lw4cMJDw/XMJGwJilkhRB5gRSzQnMVKlRg5syZ5mU/Pz/i4+M1TCSsqV27duahnB4+fMiQIUM0TiRyyo0bN/j555+1jiGEyGekmBV5Qv/+/WnRogUA58+fZ8qUKRonEtY0f/588+x/3377rfmyEmE/goKC8PDwoFu3bly5ckXrOEKIfESKWZEn6HQ6goKCcHJyAmD27NmEhIRoG0pYTalSpViwYIF5ecCAAURFRWkXSFjdiRMniIqKIjY2Fn9/f7nZTwiRa6SYFXmGh4cHY8eOBWSwfXvUs2dP2rZtC5i+jpbB9u3LjBkzKF++PABbt27lm2++0TiRECK/kGJW5CmjR4+mbt26AERERHD9+nWNEwlrURSFwMBAXF1dAVi2bBn79u3TOJWwFjc3N5YtW2ZeHjJkCPfu3dMwkRAiv5BiVmRJxN1IVo9fS/fyfrRz7s67lf3539TviH4YY5Xnd3JyIigoiI8++ojQ0FCqVKlilecVeUOVKlX45JNPzMs+Pj4kJiZqmEhYU6dOnXjnnXcAuHfvHgEBARonEkLkBzLOrMi021fuMrTlOB6ER2A0GM3rdTqFMlVKs+CPqRR3L6ZhQmELDAYDzZs358iRI4Bp9qjJkydrnEpYS3h4OHXq1CEiIgKAbdu2mS8vEULkbzLOrNDcrN6LeXg7dSELYDSq3Ll6lwX9V2qUTNgSvV5PcHAwDg4OgOlay5MnT2qcSliLu7s7c+fONS/369ePmBjrfHMjhBCWSDErMuXK6WuE7g3DkGK0uN2QYuTgT8e4c/Wu1fv++++/efPNN7l9+7bVn1too0GDBnz00UcAJCcn4+Pjg8Fg0DiVsJY+ffrw0ksvAXDlyhUmTJigcSIhhD2TYlZkyt9HL2bYRlVVzh2/ZNV+f/zxRxo0aMCmTZsYOnSoVZ9baGvcuHE888wzABw6dCjVzUPCtimKwooVK3BxccHd3Z2WLVtqHUkIYcekmBWZ4uDkYNV2mfXcc89RsGBBANauXcvmzZut+vxCOy4uLqxc+e+lKR9//DFXr17VMJGwpho1arBp0ybCwsLo0qWL1nGEEHZMilmRKQ3b1EXvkP7HxbmAE/Va1rZqv6VLl2b+/PnmZX9/f6Kjo63ah9BOq1at8PPzAyAmJkYG27czbdu2pWjRolrHEELYOSlmRaYUK1OUV3q1RtEpFrcrikJH/7YULOJq9b7ff/99XnnlFQCuX7/Oxx9/bPU+hHZmzZpF2bJlAdiyZQvr1q3TOJHISXIzmBDC2qSYFZk2aPH/0eQVTwDzWdpH/23ZpRl9Z7ybI/0+uv7u0WD7S5cuZf/+/TnSl8h9RYsWZenSpeblwYMHc//+fQ0TiZxw48YN3nzzTdq3b4/RaPlGUiGEeBoyzqzIElVVObEjlO1f7uH+zYeUqliCth+0of4LdVAUy2dtrWXevHkMHz4cgDp16nDixAmcnZ1ztE+Re9566y2+//57AHr37s3q1au1DSSsRlVVmjZtyvHjxwFYsWKF+fISIUT+kVP1mhSzwmakpKTQvHlzjh49CsCkSZOYOHGixqmEtdy8eRMPDw8iIyMB+PXXX82Xlwjbt2PHDry9vQEoUqQIYWFhlCtXTuNUQojcJJMmiHzPwcGB4OBg9Ho9ANOnT+fWrVsapxLWUq5cOWbPnm1e7tevH7GxsRomEtb08ssv06dPH8D0C23QoEEaJxJC2AspZoVN8fT0ZNSoUdSoUYNt27aZbxwS9qFv3760atUKgEuXLsmZdzszZ84cSpcuDcDGjRvNl5UIIUR2yGUGwuYkJCSgqioFChTQOorIAX///TcNGjQgMTERnU7HoUOHaNq0qdaxhJWsW7eO7t27A6apb8PCwmT4LiHyCbnMQIh/uLi4SCFrx5555hnzGVmj0YiPjw/JyckapxLW0rVrVzp06ABAeHg4o0aN0jiREMLWSTErcpUhxcAfGw8RGLCawOFrOLj5GAaDIVvPaTQa2bJliwy2b0dGjBhBgwYNAPjzzz+ZO3euxomEtSiKwrJlyyhUqBAAQUFB7N69W9tQQgib9lTF7NKlS6lSpQouLi54eXlx+PDhNNuuXr0aRVFSPVxcXFK1UVWVCRMmULZsWQoUKIC3tzfnzp17mmgiD7vw52XeqzaQyW/NYdPSbWxaspXxnWbyf7WHcO3sjad6zr///ps2bdrQvn171q9fb+XEQiuOjo4EBwej05l+RE2aNEl+JtiRihUrMnPmTPPyDz/8oGEaIYSty3Ixu27dOgICApg4cSLHjx/H09OTtm3bcufOnTT3KVKkCLdu3TI/rly5kmr7p59+yqJFiwgMDOTQoUMULFiQtm3bkpCQkPUjEnnSg/CHjHxpEg9uPQTAkGzAkGw6I3v7yl1GtJlETETW71wPDQ1l7969AHz44Yc8ePDAapmFtp599lmGDh0KQGJiIr6+vjLYvh3x9/fnrbfeYsOGDammrBZCiKzKcjE7b948fH196dOnDx4eHgQGBuLq6sqqVavS3EdRFNzd3c2PMmXKmLepqsqCBQsYN24cb7zxBg0aNOCLL77g5s2bbNq06akOSuQ9mwO3ExsVj9HwZDFiSDHy8HYkv3y+K8vP26VLFzp37gzAnTt3GDFiRHajijxkypQpVKlSBYA9e/ak+3NG2BadTsd3331Hly5dtI4ihLBxWSpmk5KSOHbsmHngazD9QPL29ubAgQNp7hcTE0PlypWpWLEib7zxBqdOnTJvu3TpEuHh4ame083NDS8vrzSfMzExkaioqFQPkbftXrfPYiH7iKqq7F63L8vPqygKS5YsMd8V+fnnn/Pbb789dU6RtxQsWJAVK1aYl0eMGCFjCwshhEglS8XsvXv3MBgMqc6sApQpU4bw8HCL+9SqVYtVq1bxww8/8L///Q+j0UiLFi24fv06gHm/rDznjBkzcHNzMz8qVqyYlcMQGoiLjs+4TVTGbSwpX748n376qXm5X79+xMXFPdVzibzn1VdfpVevXgBERkby4YcfapxI5ARVVdmwYQPBwcFaRxFC2JgcH82gefPm9OrVi4YNG9KqVSu+//57SpUqlepsS1aNGTOGyMhI8+PatWtWTCxyQpW6FdHp0/646Rx0VK1f6amf39fXlxdffBGAixcvMmnSpKd+LpH3zJs3j1KlSgGwYcMGNm7cqHEiYU0Gg4G3336bt99+m8GDB3Px4kWtIwkhbEiWitmSJUui1+u5fft2qvW3b9/G3d09U8/h6OhIo0aNOH/+PIB5v6w8p7OzM0WKFEn1EHlbR/+26V5mYEwx0qH/q0/9/DqdjpUrV+Ls7AzA3LlzOX78+FM/n8hbSpQowcKFC83LAwcOJCIiQrtAwqr0ej3lypUDID4+nv79+8tQe0KITMtSMevk5ESTJk3YsWOHeZ3RaGTHjh00b948U89hMBgIDQ01T0NatWpV3N3dUz1nVFQUhw4dyvRziryveaemtOnREpTHNvyz3KHfK3i2rputPmrVqsWECROAfwfbT0lJydZziryje/fuvP766wDcunWL0aNHa5xIWNP06dOpUKECANu3b+fLL7/UOJEQwlZk+TKDgIAAgoKCWLNmDWFhYfj7+xMbG0ufPn0A6NWrF2PGjDG3nzJlCr/++isXL17k+PHjvPfee1y5cgUfHx/AdAPP0KFDmTZtGj/++COhoaH06tWLcuXKme9SF7ZPp9Px0ReD6D+nN6UqljCvL1u1DIOX+jB4mS+K8nilm3UjR46kfv36ADRp0oTExMRsP6fIGxRFYfny5RQsWBCAFStWmIdlE7avcOHCLF++3Lw8bNiwdId8FEKIRxyyukO3bt24e/cuEyZMIDw8nIYNG7Jt2zbzDVxXr141D3QO8PDhQ3x9fQkPD6dYsWI0adKE/fv34+HhYW4zatQoYmNj8fPzIyIigpYtW7Jt27YnJlcQtk2v1/PWsA68OeR17t98iKJAiXLFrVLEPuLo6Mjq1auJjIykTZs2gOnGkn379rFlyxaOHTvGjRs3UBSFihUr0qRJEzp27EizZs2slkHknEqVKjFjxgwGDx4MmK6V/vPPP+VnhZ3o0KED3bt3Z+3atTx48IChQ4fy9ddfax1LCJHHKaodXJgUFRWFm5sbkZGRcv2sSOWXX35h5MiRhIaG4u7uzrPPPmset/TChQscOXKEu3fv0rRpU+bMmUOrVq20DSwyZDAYaNmyJQcPHgRg7NixTJs2TeNUwlru3LlDnTp1zBOgbN68mfbt22ucSghhDTlVr+X4aAZCaCEpKYl+/frx2muvUbJkSbZu3cqNGzf48ccfWbRoEYsWLeLnn3/m1q1bbN68GScnJ1q3bk1AQAAGg0Hr+CIder2e4OBgHB0dAZg1axZ//fWXxqmEtZQuXZp58+aZl/39/YmOjtYwkRAir5NiVtid5ORkunbtyurVq1m+fDndunXD39/f4vV3er2e9u3b8/vvvzNv3jwWLVpE7969ZdrUPK5u3brma/NTUlLw8fGRP0LsSK9evcwT6Vy7do3JkydrnEgIkZdJMSvszpQpU/j555/ZuHEjDx48oH///ly+fNl8naUlOp2OYcOG8fXXX/P111+nOjMk8qaPP/6Y2rVrA3DkyBEWL16scSJhLYqisGLFCgoUKEDnzp0JCAjQOpIQIg+Ta2Y1lJKcwq2Lt1EUhbLVy6DX63Ot75jIGPasP4BO0dH63ZYUKOCcpf0f3okk4k4kxd2L4lYya695Th53SEgITZs2Zfz48UycOJF79+5Rp04d7t27B8D3G76nad1m6fYdEBDAsmXLCA0NpWbNmlbLJqxv3759tGzZEgBXV1dOnTplviZa2L4LFy5QvXp1rWMIIawkp+o1KWY1kJKcwrpZP7Bx8RYi70YBULxsMd4e1oEuw9rnaFEbExnDgMajuXUp9SQVlWqXZ8mxWRkWtX8fu8DnY7/h6PY/QTWdQfHq0Jj/++RdqtZLfwav3Djud999l8OHDxMWFma+pvKrr77ivffeA6CAzhUv4ys4KI5p9h0fH0/16tV54403Ug0VJPKmgQMHsmzZMgDatm3L1q1brTpChhBCCOuQYjYdtlTMGgwGprw9lwM/Hn1yhhsFvHu+yKg1g3Lkl3F8fCJvFf+A5ETLEwm4FHRmU8SaNIvKk/vOMMp7MoYUY6rZvHR6HY7Ojiz4fSo1GlW1uG9uHPf9+/cpW7YsM2fOTPW1ZEpKCrXL1+XCnb8BqEB1aiuN0u174sSJzJs3j9u3b+Pq6vrUmUTOi4qKwsPDgxs3bgDw5Zdfmv94Efbl4cOHREVFUblyZa2jCCGegoxmYCf++P4w+384YnmqRhV++99ejm3PmTuzp70zL81CFiAhNpF5voEWt6mqyty+yzAkG56YltZoMJKcmMyC/ivTfO7cOO5Dhw6RnJzMG2+8kWr9vo1HcL9THT2mIv06F4hQ76Xbd+fOnYmJiSEkJCRbmUTOK1KkiPnMLMDQoUO5e/euhomEtamqyoYNG/Dw8KBnz55yg6YQIhUpZnPZT4G/oNOn/bLrHHRsXvFrjvR99NeQDNvs+mafxfWn9p/l+t+3MBotn8g3GoycPXKeSyevWtyeG8cdEhJC0aJFqVat2hN9F3QoTHXqmded5hhG1ZBm33Xr1sXJyUmKWRvRqVMnunbtCpjO0A8bNkzjRMKakpKSGD16NOHh4ezbt48VK1ZoHUkIkYdIMZvLrp+9+cSZzf8yphi5FnYjR/o2pmR8NiM5Mdni+hvnbmWqj7Ta5cZxR0ZGUqJEiScuVXjUd0VqUIRiAMQRzSXOpNm3k5MTbm5uPHz4MFuZRO5ZuHAhRYsWBUzXSW/dulXbQMJqnJ2dWbny329+PvroI65fv65hIiFEXiLFbC4r6FYw/QYKFCyaQZscpOgsX7Na0C1z1426FrHcLjeO28nJiYSEhDT7VhQFD5qiYDrGm1zGoBos9q2qKgkJCTg5OWUrk8g97u7uzJ0717zs7+9PTEyMhomENbVp04a+ffsCEB0dzYABAyxftiSEyHekmM1lL73bMs2C8b9tckLZamUybFOtgeUbK5q86olLwfRHOnArWZj6L9S2uC03jrtWrVrcuHHjibOp/+27kOJGFWpTjqo8xyvoFb3Fvq9evUp0dDR16tTJViaRu/r06cNLL70EwJUrVxg/frzGiYQ1zZ49mzJlTD/HfvrpJ7777juNEwkh8gIpZnNZez9v3EoWsXj9qM5BR6kKJXi1d+sc6XvUmkEZthm71vK1hgUKuvDux2+lu+/7E7vi6ORocVtuHPezzz4LwJ49e9LtuxoeeChNcFSc0uz70XM0bdo0W5lE7no02L6LiwtguvTg0KFDGqcS1lKsWDGWLFliXh40aBAPHjzQMJEQIi+QYjaXFS3lxtzdk81nSfUOevQOprODlWqXZ+7uybgWLpAjfdd7vjZDlvtZ3qjAx98MoeIz5dLcv/vozvQc+xZ6Bx2KTsHBUW/+b9/p79JpQNs0982N437mmWdo0qTJEzeHPN63g6NDhn2vWLGCl19+GXd392xlErmvRo0a5ulPVVXF19eXpKQkjVMJa3nrrbfMI5bcuXOHkSNHapxICKE1GWdWI0ajkRM7QgndGwYKNHqpPg1aeeTKYO9JSUks9g82DUelwHMdmzJwYZ9MT1rw8HYEu9bu42F4BCUrlKBN9+cpUqJwpvbN6eP+4osv6N27N7/99hsvv/xypvq+desWa9asYfTo0SiKwsaNG+nSpQubNm16YpgvYRtSUlJo1qwZJ06cAGDatGmMHTtW41TCWm7cuIGHhwdRUabJVyz9exdC5D0yaUI6bLGYFTnDaDTy8ssvc/HiRY4cOULp0qXTbf/NN9/g7+9PZGQkX3/9NS1btqRp06Y899xzbNq0SWaSsmHHjx+nWbNmGAwGnJyc+PPPP6ld2/I13cL2BAYG4u/vj5ubGytXrjQPzSaEyLtk0gQhMkGn07F69WqSkpJ4+eWXMxy+x9XVlcjISMA0LWrr1q0pUKAAK1askELWxjVu3Ng8E1xSUhJ+fn4y2L4d8fPzY/z48Zw+fVoKWSHyOTkzK+xSWFgYr776KtHR0cyZM4fevXvj6Gj55rQuXbqwceNGAAoVKsSff/75xMQLwjbFxcVRv359Ll68CJjO5vXr10/jVEIIkT/JZQbpkGJWWPLw4UOGDh3KF198QdmyZenevTvNmjWjatWqqKrKhQsXOHToEF999VWqO6J/+eUXXn31VQ2TC2vasWMH3t7egGnq29OnT1O+fHmNU4mcoqqqfKsiRB4lxWw6pJgV6QkNDWX58uVs2bKFK1eupNpWrVo1OnToQKlSpcxjklapUoWTJ09SsKB2k1cI6/q///s/Pv/8cwDeeOMNNm7cKAWPnXn48CGjRo2iVq1ajBgxQus4QggLpJhNhy0Ws6cPnGXFyC+4FHoVBYWajavhN7cXzzTO+Ovt+Jh4ZvVewuEtx0lJNuDo5EDLLl4MX+WfqRmr7ly9y+YV2/lrz2kUnULjlxvQzvdlSpYrbo1Dy7N9A9y7d4+bN2+iKArly5eneHFTv6qq8vLLL7Nr1y4AAgICUs0mJWzbgwcPqFOnDnfu3AHgu+++46230h83WdiORxOc3LhxAxcXF0JDQ6lRo4bWsYQQj5FiNh22VsyuHPUl6+f8aHHbB9O60zOdyQluXrhFn9pDMRqevJHFwVHPV1eWU9y9WJr7//79Iab3mI/RqJqfQ6fX4eCoZ9LGUTzbtmHWDiYLtOw7M86dO0eDBg1ISEhAp9Nx8OBB80QMwvZ9++23dOvWDTBNfXv69GmKFUv734qwLUOHDmXhwoUAvPTSS/z2229y9l2IPEZGM7ATBzYfS7OQBVg9bi2nD5xNc3u/hiMtFrIAKckGfOoFpLnvlbDrfNJ9PikphlTPYTQYSU5MYeKbn3Ln6t1MHEXWadl3ZtWsWZOJEyeachmN+Pj4kJKSomkmYT3vvPMOHTt2BCA8PJxRo0ZpnEhY07Rp06hUqRIAO3fuZPXq1doGEkLkGilmc9mqMV9l2CZwxBcW1x/+JYSE2MR0941+EMP5kEsWt/2wZBsqKlg4F6+qKoZkAz8Fbs8w39PQsu+sGD58OJ6enri7uzNhwoRMTyQh8j5FUVi2bBmFC5sm+AgODjZfViJsX6FChQgMDDQvDx8+nNu3b2uYSAiRW6SYzWVXz9zIsM2FE5aL0Q1z0z6j+1/ffvqDxfWHtx7HmJL2OJtGg5EjW49nqo+s0rLvrHB0dGT9+vWcPn2at956S76mtDMVKlRg5syZ5mU/Pz/i4+M1TCSsqV27dvTs2RMw3RA2ZMgQjRMJIXKDFLN5UFqXMRvSKQb/KyXJ8lfj6RWT5n0z2UdWadl3VtWsWVOupbRj/fv3p0WLFgCcP3+eKVOmaJxIWNP8+fMpUaIEAOvWreOnn37SOJEQIqdJMZvLSpbP+K79ctXdLa5/uecLmerjtf9rY3F9vZa10Tuk/ZbrHXQ0eKFOpvrIKi37toarV69qHUFYiU6nIygoyDzyx+zZswkJCdE2lLCaUqVKsWDBAvPygAEDiIqK0i6QECLHSTGby94d2yXDNh9M7W5xfbu+L6N3SP8aTicXR5q93sTits4ftkv37K7BYKTjgLYZ5nsaWvadHTdv3qRLly7Ur1+fmzdvah1HWImHhwdjx44FwGAwyM1+dqZnz560bWv6eXL9+nWCg4M1TiSEyElPVcwuXbqUKlWq4OLigpeXF4cPH06zbVBQEC+88ALFihWjWLFieHt7P9H+gw8+QFGUVI/XXnvtaaLlee19X8GrfeM0t7/0bktavumV5vYpP41O9/nn7U77K1OP5rXw+/R9gFRnSfUOOlBgyDI/qtarlO7zPy0t+86OqVOnsnHjRqKiohg0aJDWcYQVjR49mrp16wJw7Ngx87BOwvYpikJgYCDlypVjxYoVDB06VOtIQogclOVxZtetW0evXr0IDAzEy8uLBQsWsH79es6ePUvp0qWfaN+zZ0+ef/55WrRogYuLC7NmzWLjxo2cOnXKPKXkBx98wO3bt80z9AA4Oztn+rpFWxtnFmDj4i2snbmJB+EPAShVoQTvjX+b1328M9z3Yuhlprwzjxt/3zKvq1y3AlM2jaJc9bIZ7v/X3tN8v/Bn/tp9GhRo/EoDugxpj8dzzzz9AWWSln0/jfv37+Ph4WEebH/Dhg106ZLx2XVhGw4cOMDzzz+PqqoUKFCAkydPUq1axhOXCNuQlJSUqYlkhBC5I89MmuDl5cWzzz7LkiVLANN4nBUrVuTDDz9k9Oj0zxqC6Su9YsWKsWTJEnr16gWYitmIiAg2bdqU9SPANotZYTvWrl1Ljx49AChbtiynT5+maNGi2oYSVjN48GAWL14MgLe3N7/++quMYiGEEDkgT0yakJSUxLFjx/D2/vfsoU6nw9vbmwMHDmTqOeLi4khOTjZPI/rI7t27KV26NLVq1cLf35/79+9nJZoQOaZbt260b98egFu3bvHRRx9pnEhY0yeffELFihUB+O233/jiC8vjPAvbt3v3bk6fPq11DCGElWWpmL137x4Gg4EyZcqkWl+mTBnCw8Mz9RwfffQR5cqVS1UQv/baa3zxxRfs2LGDWbNmsWfPHtq1a4fBYLD4HImJiURFRaV6CJFTHg22X6hQIQBWrlzJnj17NE4lrKVw4cIsX77cvDxs2DAZbN/OREdH4+vrS5s2bejbt2+av1uEELYpV0czmDlzJmvXrmXjxo24uLiY13fv3p1OnTpRv359OnfuzObNmzly5Ai7d++2+DwzZszAzc3N/Hh0VkWInFKpUiVmzJhhXvb19SUhIUHDRMKa2rdvb76U5OHDh3LDkJ1xdHRk7969ABw8eDDVHy9CCNuXpWK2ZMmS6PX6J85a3L59G3d3y2OjPjJnzhxmzpzJr7/+SoMGDdJtW61aNUqWLMn58+ctbh8zZgyRkZHmx7Vr17JyGEI8FX9/f5o3bw7AuXPnmDp1qsaJhDUtWLDAfPnT2rVr2bx5s8aJhLW4uLgQFBRkXh4zZoyMHS2EHclSMevk5ESTJk3YsWOHeZ3RaGTHjh3mX/KWfPrpp0ydOpVt27bRtGnTDPu5fv069+/fp2xZy3fmOzs7U6RIkVQPgJBdJ3P166OEuAS+mvYd83wDWffpJpKSkrO0/8bFP+NTbxi+DQLY+tlvWdr38plrvFm8N685d6dLqf/jzj+jImTWpx8spkOh9+hQ+D0WfxiU8Q7/ce/WPfo1GkHXsr4MbDaayMjYLO1/69Jtdnz1Ozu//p071+5lad/syk7fer2eoKAgHB0dAfj777/TnK1N2J7SpUszf/5887K/vz/R0dEaJhLW9OKLL9KvXz8AYmJi8Pf3l3+/QtiJpxqaq3fv3qxYsYJmzZqxYMECvv32W86cOUOZMmXo1asX5cuXN38lO2vWLCZMmMDXX3/N888/b36eQoUKUahQIWJiYpg8eTJvvfUW7u7uXLhwgVGjRhEdHU1oaCjOzs4ZZnp0d1xr3qBM+dIMXNSXF7qkPVarNSz0X8nPK39L9cNQ0Sm8HdDRPJ5qWg5uPsb4N2bCY6+8olNY8PtUPJrXSnf/di7dSUl6smh3cnXi55iv0t133aebCB5tuc3oLz/k5Z4vprt/9wp+3L/5ZOFctnoZvji3JN19I+5GMrfvcg7+fMx87Iqi8OI7zzFsRT8KuhVMd//ssGbfCxYsoEKFCrz11lty17udUVWVtm3bsn37dgAGDRpkHulA2L7IyEjq1KnDrVumYQ2/+eYbune3PEmNEML68szQXABLlixh9uzZhIeH07BhQxYtWoSXl6l4bN26NVWqVGH16tUAVKlShStXrjzxHBMnTmTSpEnEx8fTuXNnTpw4QUREBOXKlePVV19l6tSpT9xolpb/FrMOOtNZs0kbRvJ852ZZPbRMWei/ks0rtqe5vdtHnfGZ0dPitsunruJbf3i6z//NzZWUdLc8xm5ahewjLgWd+Sn6fxa3HfnlBB+3m55u30En51HFw/I1yGkVso+Ur1mW1WcXWdwWH5vAh15juHb2JkZD6pnAdHodNRtXZf7vU3F0ckw339PQsm9hey5dukS9evWIi4tDURT++OMPWrRooXUsYSWbNm3izTffBExT34aFhVGiRAmNUwmRP+SpYjavSVXMKo6ggHuV0qw5txidzrr3uCXEJdCpcK90v57SO+jYHPcVDg4OT2x7t3J/7l5Lf9ixyh4VCD45/4n1l89cw9cjIMOM6x8EWRwHtZ1zd1KS078Mw9nVmc0xTxbD927do0d5/wz7/iFmDa6urk+s/3HZLyz+MPiJs9H/NfrLwbzc84UM+8gqLfsWtmnevHkMH276o7NOnTqcOHEiU98SCdvw9ttvs2HDBgB69+5tPvkihMhZeWKcWZuhQvilO4QdPGf1p94wb3OG11kZUoxs+2yXxW0ZFbIAV05ft7h+WItxGQcEBjYcY3F9RoUsQGJcosX1Y1+fmam+J3eea3H9ts93kt4X8jqdwrbPd2aqj6zK6b6///57PvjgA7n+zo4MHjzYfH1/WFgYM2dm7vMvbMPixYtxc3MDYM2aNebLSoQQtsk+i9l/PAiPsPpzhl++m6l2Ny9kbtzdrIiPsVxoPi7invXH3c3sa3nrkuXxOR/cfEh6tZ7RqHL/xoOnSJaxnOx70KBBvPXWW6xZs4avvkr/emVhOxwcHAgODkav1wOmiRVksH37UbZsWWbPng2YbgyrXLmyxomEENlh18VsyfLFM26URWWrZ+463oq1y1u9b9ciLhk3AkqUtXy9bXZk9rWsWMvycZesUDzdm6V0eh2lK5V8qmwZycm+X331VfP/Dx06lLt3M/fHjsj7PD09GTlyJADJycn4+vpiNBoz2EvYir59+/L999+za9cunnnmGa3jCCGywS6LWUVRKF+zLLWb1bD6c789rAOKLv072PWOetp+0NriNvcqpTLso3rDKhbXLzuaua86V4bOsbjewenJa3gfV6Cw5YJ55raxmep70sYRFte36/tyul/DGw1G2vV9OVN9ZFVO9t2pUye6du0KwP379xk2bNhTPY/ImyZMmECNGqafI/v37ycwMFDjRMJadDodb775ptXvqxBC5D67+1esKAooMHBhnxwZNsnJxYkuQ9un2+b98W+n+QNy5q/jM+xj5q+WC0f3Ku44FXBKd99CRV1Tza72X5/+NiHDvpcdn2VxvVtJN9yrlk5332qelc1jsD7O+/0XqdGoKjr9k6+LTq+jXsvaPP9mzow+kdN9L1q0iGLFTGfDv/rqK7Zu3frUzyXylgIFCrBy5Urz8ujRo2WSFjsWHx9PcnLWxgsXQmjP7orZstXL8MnmMTz7WqMc66P/nN50HfXGE8WR3lHPB1O60XPc22nuW75GWeb/PsViYaV31BEUOpeiJYumuf/PsV/h7Gq5oC1U1JWND9akuW/9lnUY+XnaIxJM3/oxFaqXS3P7lxeWUq6G5ZneqnlWZsUJy2eEAZwLODN7x0RadW2R6tj1Dnpe7d2K6VvH4uCY8Znjp5HTfZcpU4Z58+aZl/v3709MTEy2Mou8o02bNvj4+AAQHR3NgAED5GY/O7Rr1y4aNGjA3LmWb2IVQuRddjU016HfjvHsS41ybSD7lJQUtgTtIPzibSrUKsdr//dSlr6y2vvdfjbM2ww6hR6ju/BchyaZ3jciIgJ/z9FE3YumuLsbQafmpXlG1pI1E9fyw5JtoCh0G/UG3UZ1zvS+cXFxTOw0m9uX71DhmXJM/mFUmmdkLXkQ/pAzh86DAh7Nn6FoKbdM75tdOdW3qqq88sor5tnxhgwZwoIFC6zy3EJ7Dx8+xMPDg/Bw042d69atM19eImzf1atXqVatGgaDAWdnZ0JDQ6lZs6bWsYSwOzLObDpy6sURIisuXLhA/fr1iY+PR1EUDhw4YJ5MRNi+DRs28Pbbpm9dSpcuTVhYGMWLW/8mU6GNESNGmM/KtmrVip07d8r1tEJYmYwzK0QeV716daZMmQKYztQ+GnRf2IcuXbrQuXNnAO7cucOIEZZvdhS2afLkyVStWhWAPXv2sGrVKo0TCSEyS87MCmFFKSkpeHl5UbFiRZYuXUr58tYfok1o58aNG3h4eBAVZRrLefv27Xh7e2ucSljL9u3bzcPtubm5ERYWRtmyZTVOJYT9kDOzQtgABwcHdu7cycaNG6WQtUPly5fn008/NS/369ePuLg4DRMJa3rllVfo3bs3AJGRkXz44YcaJxJCZIYUs0JYmZubW67dhChyn6+vLy+88AIAFy9eZNKkSdoGElY1d+5cSpUyjQe+YcMGNm7cqHEiIURG8vVlBoe3nmDD/J84+ccZFEXB86V6vD2sA41eqp+DabPvSth1Fvit4NSBs6hGFZ1eR4MXPQgI7k/ZqhnPUPbl1O/YuPBnoh+Yho9yK1WEbqPe4J3hnXI6er4UERFBSEgIrVu31jqKsJKzZ8/SoEEDkpKS0Ol0HDlyhMaNG2sdS1jJ2rVr6dGjB2Ca+vb06dMULVpU21BC2AG5zMDKvpy8nrHtpxOy6xRJCckkxidx7JcQRnlPYcP8zVrHS9OpfWfwqx/AyX1nUI2mv0OMBiMhu07Sp9YQLv51Od39R7SZxBcT15kLWYDIu1GsHPklEzpbnjBBPL3vv/+eOnXq0KlTJ27cuKF1HGEltWrVYsIE0yQkRqORvn37ymD7dqRbt260b2+aHOfBgwccOHBA40RCiPTky2L2r72n+WLyt4CpEHzEkGL6/8DhazgfckmTbBn5uMMMjEbLJ9MNKQZGt52W5r4bF2/hzz2n0tx+4Mej7Fq3L9sZxb+2bNlCeHi4DLZvh0aOHEn9+qZvcUJCQpg/f77GiYS1KIrCsmXLeP311/nzzz9p166d1pGEEOnIl8XsD0u2ondI+9D1Djp+XLotFxNlzoGfjhAXmf7NJg9vR3L6wFmL27799IcM+/hi4rqnyiYsmz17NmXKmC79+PHHH9mwYYPGiYS1ODk5ERwcbL4+euLEiZw/f17jVMJaKlWqxM8//0ytWrW0jiKEyEC+LGZPH/jbfBbWEkOKkVMH/s7FRJlzcPPxTLXb/+NRi+sf3HqY4b7hl+9mKZNIX7FixVi8eLF5edCgQTx8mPH7IGxDs2bNGDJkCAAJCQn4+fnJ2XchhMhl+bKYdXR2yLiNU8Ztcpujc+amjHVysdxO0WV8h71Ony8/Ejnq7bffplMn0811t2/fZuTIkRonEtY0depUKleuDMCuXbv4/PPPNU4kckJ8fDxjxozhr7/+0jqKEOIx+bJyea5DU3TpXGag0+to3rFpLibKnPZ+mRucvV3fly2ur1KvUob71mpaPUuZRMYURWHp0qUULlwYgM8++4ydO3dqnEpYS6FChQgMDDQvDx8+nPDwcA0TCWs7f/48DRo0YObMmfj6+mIwGLSOJIT4j3xZzHYa+Bo6nQ5LQ4EqOgVHZ0fa93sl94NloGq9SpSvmf5sNDWbVKNUhRIWt/nMei/DPvrP/+BpookMVKhQgVmz/h0tws/Pj/j4eA0TCWt67bXXeO8907+viIgIBg8erHEiYU0VK1bEwcH0bd3hw4dZsmSJxomEEP+VL4vZCjXLMun7kTg6O6b66l3RKbi4OvPJ5jGULFdcw4RpW3TgE4qUKGxxW4nyxZi3Z3Ka+zZ9xZP3J76T5vb+83rzTONq2c4oLOvXrx/PP/88ABcuXGDKlCkaJxLWNH/+fEqWLAnA+vXr+eGHjG+4FLbB2dmZoKAg8/LYsWO5fPmydoGEEKnk60kTHt6JZNtnOwn9/TQoCo3a1KNtnzZpFot5hdFoZP2cH9m8cjsxD2MpUqIwnT9sxxv/nHHOyOVT1wgcvoZzxy+iKAq1m9XAf94HGZ71FdkXFhZGw4YNcXFxYc6cOfj6+modSVjRV199ZT5DW65cOU6fPo2bm5vGqYS1DBgwgOXLlwPQtm1btm7dKrP9CZEFOTVpQr4uZoXQwnfffUfz5s0pX7681lGElamqyuuvv862baah/fz9/Vm2bJnGqYS1REZGUrduXfMEKP/73//o2bOnxqmEsB1SzKZDilkhRF5x5coV6tatS2xsLAC///47LVu21DiVsJYff/yRN954A4ASJUoQFhZGqVKlNE4lhG2Q6WyFsGMyFar9qFy5Mp988ol52dfXl4SEBA0TCWvq1KkT77xjuvfg/v37BAQEaJxICCHFrBAaioiIwM/Pj9dff10G27cjgwYNolmzZgCcOXOG6dOna5xIWNOiRYsoWrQoYLrUYNeuXdoGEiKfk2JWCI2oqkq7du0ICgrit99+Y82aNVpHElai1+sJDg42D+c0Y8YMQkNDNU4lrMXd3Z25c+fi7OzM9OnT5TISITQm18wKoaGtW7fy+uuvA6apb8PCwihTpozGqYS1jB8/nmnTpgHg5eXFvn370Ov1GqcS1qCqKleuXKFKlSpaRxHCZuSpa2aXLl1KlSpVcHFxwcvLi8OHD6fbfv369dSuXRsXFxfq16/Pli1bUm1XVZUJEyZQtmxZChQogLe3N+fOnXuaaELYlHbt2vHuu+8C8PDhQ4YMGaJxImFNY8eOpVatWgAcOnSIpUuXapxIWIuiKFLICpFHZLmYXbduHQEBAUycOJHjx4/j6elJ27ZtuXPnjsX2+/fvp0ePHvTt25cTJ07QuXNnOnfuzMmTJ81tPv30UxYtWkRgYCCHDh2iYMGCtG3bVm6aEPnCggULKFHCNGvbunXr+OmnnzROJKzFxcUl1WD7H3/8MVeuXNEwkchJoaGhJCUlaR1DiPxHzaJmzZqpAwcONC8bDAa1XLly6owZMyy279q1q9q+fftU67y8vNR+/fqpqqqqRqNRdXd3V2fPnm3eHhERoTo7O6vffPNNpjJFRkaqgBoZGZnVwxEiT/jiiy9UQAXUChUqyGfZzvTv39/8/rZr1041Go1aRxJWFBcXp3700UeqXq9Xp02bpnUcIfKsnKrXsnTNbFJSEq6urnz33Xd07tzZvL53795ERERYnL6xUqVKBAQEMHToUPO6iRMnsmnTJv78808uXrxI9erVOXHiBA0bNjS3adWqFQ0bNmThwoVPPGdiYiKJiYnm5cjISCpVqsS1a9fkmllhk1RVpUuXLuzcuRMwDec0Z84cjVMJa4mMjKRZs2aEh4cDEBQURNeuXTVOJazlr7/+olWrVhiNRhwdHdm/fz/PPPOM1rGEyHOioqKoWLEiERERVp0d0SErje/du4fBYHjiBpUyZcpw5swZi/uEh4dbbP/oh/qj/6bX5nEzZsxg8uTJT6yvWLFi5g5EiDwuKCgo1dfTwr74+vrKVMZ2Kjk5mWeffVbrGELkaffv39eumM0rxowZk2qg6oiICCpXrszVq1dlHvQsePQXkpzRzjx5zZ6OvG5ZJ6/Z05HXLevkNXs68rpl3aNv0osXL27V581SMVuyZEn0ej23b99Otf727du4u7tb3Mfd3T3d9o/+e/v2bcqWLZuqzX8vO/gvZ2dnnJ2dn1jv5uYmH6inUKRIEXndskhes6cjr1vWyWv2dOR1yzp5zZ6OvG5Zp9NZd5qDLD2bk5MTTZo0YceOHeZ1RqORHTt20Lx5c4v7NG/ePFV7gO3bt5vbV61aFXd391RtoqKiOHToUJrPKYQQQgghBDzFZQYBAQH07t2bpk2b0qxZMxYsWEBsbCx9+vQBoFevXpQvX54ZM2YAMGTIEFq1asXcuXNp3749a9eu5ejRo6xcuRIwjdU3dOhQpk2bRs2aNalatSrjx4+nXLlyqW4yE0IIIYQQ4nFZLma7devG3bt3mTBhAuHh4TRs2JBt27aZb+C6evVqqtPHLVq04Ouvv2bcuHF8/PHH1KxZk02bNlGvXj1zm1GjRhEbG4ufnx8RERG0bNmSbdu24eLikqlMzs7OTJw40eKlByJt8rplnbxmT0det6yT1+zpyOuWdfKaPR153bIup14zu5jOVgghhBBC5E/WvQJXCCGEEEKIXCTFrBBCCCGEsFlSzAohhBBCCJslxawQQgghhLBZNlHM7t27l44dO1KuXDkURWHTpk0Z7rN7924aN26Ms7MzNWrUYPXq1TmeM6/J6uu2e/duFEV54pHWtML2aMaMGTz77LMULlyY0qVL07lzZ86ePZvhfuvXr6d27dq4uLhQv359tmzZkgtp84anec1Wr179xOcss6OX2Ivly5fToEED84DrzZs3Z+vWrenuk58/Z5D110w+Z0+aOXOmeUjM9OT3z9rjMvO6yecNJk2a9MRrULt27XT3scZnzSaK2djYWDw9PVm6dGmm2l+6dIn27dvTpk0bQkJCGDp0KD4+Pvzyyy85nDRvyerr9sjZs2e5deuW+VG6dOkcSpj37Nmzh4EDB3Lw4EG2b99OcnIyr776KrGxsWnus3//fnr06EHfvn05ceIEnTt3pnPnzpw8eTIXk2vnaV4zMM2a89/P2ZUrV3Ipcd5QoUIFZs6cybFjxzh69CgvvfQSb7zxBqdOnbLYPr9/ziDrrxnI5+y/jhw5wooVK2jQoEG67eSzllpmXzeQzxtA3bp1U70Gf/zxR5ptrfZZU20MoG7cuDHdNqNGjVLr1q2bal23bt3Utm3b5mCyvC0zr9uuXbtUQH348GGuZLIFd+7cUQF1z549abbp2rWr2r59+1TrvLy81H79+uV0vDwpM6/Z559/rrq5ueVeKBtRrFgxNTg42OI2+ZxZlt5rJp+zf0VHR6s1a9ZUt2/frrZq1UodMmRImm3ls/avrLxu8nlT1YkTJ6qenp6Zbm+tz5pNnJnNqgMHDuDt7Z1qXdu2bTlw4IBGiWxLw4YNKVu2LK+88gr79u3TOo6mIiMjAShevHiabeTzllpmXjOAmJgYKleuTMWKFTM8u2bvDAYDa9euJTY2Ns1pvOVzllpmXjOQz9kjAwcOpH379k98hiyRz9q/svK6gXzeAM6dO0e5cuWoVq0aPXv25OrVq2m2tdZnLcszgNmC8PBw84xkj5QpU4aoqCji4+MpUKCARsnytrJlyxIYGEjTpk1JTEwkODiY1q1bc+jQIRo3bqx1vFxnNBoZOnQozz//fKoZ6x6X1uctP11r/EhmX7NatWqxatUqGjRoQGRkJHPmzKFFixacOnWKChUq5GJibYWGhtK8eXMSEhIoVKgQGzduxMPDw2Jb+ZyZZOU1k8+Zydq1azl+/DhHjhzJVHv5rJlk9XWTzxt4eXmxevVqatWqxa1bt5g8eTIvvPACJ0+epHDhwk+0t9ZnzS6LWfF0atWqRa1atczLLVq04MKFC8yfP58vv/xSw2TaGDhwICdPnkz3eh+RWmZfs+bNm6c6m9aiRQvq1KnDihUrmDp1ak7HzDNq1apFSEgIkZGRfPfdd/Tu3Zs9e/akWZyJrL1m8jmDa9euMWTIELZv357vbkbKjqd53eTzBu3atTP/f4MGDfDy8qJy5cp8++239O3bN8f6tcti1t3dndu3b6dad/v2bYoUKSJnZbOoWbNm+bKYGzRoEJs3b2bv3r0Z/kWd1ufN3d09JyPmOVl5zR7n6OhIo0aNOH/+fA6ly5ucnJyoUaMGAE2aNOHIkSMsXLiQFStWPNFWPmcmWXnNHpcfP2fHjh3jzp07qb5dMxgM7N27lyVLlpCYmIher0+1j3zWnu51e1x+/Lw9rmjRojzzzDNpvgbW+qzZ5TWzzZs3Z8eOHanWbd++Pd3rqoRlISEhlC1bVusYuUZVVQYNGsTGjRvZuXMnVatWzXCf/P55e5rX7HEGg4HQ0NB89VmzxGg0kpiYaHFbfv+cpSW91+xx+fFz9vLLLxMaGkpISIj50bRpU3r27ElISIjFgkw+a0/3uj0uP37eHhcTE8OFCxfSfA2s9lnL0u1iGomOjlZPnDihnjhxQgXUefPmqSdOnFCvXLmiqqqqjh49Wn3//ffN7S9evKi6urqqI0eOVMPCwtSlS5eqer1e3bZtm1aHoImsvm7z589XN23apJ47d04NDQ1VhwwZoup0OvW3337T6hBynb+/v+rm5qbu3r1bvXXrlvkRFxdnbvP++++ro0ePNi/v27dPdXBwUOfMmaOGhYWpEydOVB0dHdXQ0FAtDiHXPc1rNnnyZPWXX35RL1y4oB47dkzt3r276uLiop46dUqLQ9DE6NGj1T179qiXLl1S//rrL3X06NGqoijqr7/+qqqqfM4syeprJp8zyx6/K18+a5mT0esmnzf1/9u5YxRFoigKoHbyMRZFRFBQMDFxByVo5AYMC1yB4Cpchbm5qZiYugLB3E28DoYWpluYcaB1/sw5UFFR8Hjc4PIpfqxWqzgcDnG5XOJ4PMZ0Oo16vR7X6zUivi9rWZTZjyujPj9lWUZERFmWURTFl29Go1GklKLX68Vms3n63K/26N7W63X0+/2oVqtRq9ViPB7Hfr9/zfAvcm9flUrlp/wURXHb4YftdhuDwSBSSjEcDmO32z138Bf6k50tl8vodDqRUopmsxmz2SxOp9Pzh3+hxWIR3W43UkrRaDRiMpncSlmEnN3z6M7k7L7PpUzWfs+v9iZvP65BbbVakVKKdrsd8/k8zufz7f13Ze0tIuKxs1wAAPg7/JP/zAIA8H9QZgEAyJYyCwBAtpRZAACypcwCAJAtZRYAgGwpswAAZEuZBQAgW8osAADZUmYBAMiWMgsAQLaUWQAAsvUOUKWbhBgD3KIAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 92
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 数据标准化的影响\n",
    "\n",
    "![](./SVM/2.png)"
   ],
   "id": "ba0e16bd0ac4e3b6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:48:03.968598Z",
     "start_time": "2025-04-10T09:48:03.962090Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = np.array([[1, 50], [3, 80], [5, 20], [5, 60]])\n",
    "y = np.array([0, 1, 0, 1])"
   ],
   "id": "a683fc95493844c5",
   "outputs": [],
   "execution_count": 93
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:48:04.304235Z",
     "start_time": "2025-04-10T09:48:04.294436Z"
    }
   },
   "cell_type": "code",
   "source": [
    "svm_clf_unscaled = SVC(kernel='linear', C=float('inf'))\n",
    "svm_clf_unscaled.fit(X, y)"
   ],
   "id": "29355bcd1aedd280",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ],
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 94
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:48:04.759686Z",
     "start_time": "2025-04-10T09:48:04.754326Z"
    }
   },
   "cell_type": "code",
   "source": "from sklearn.preprocessing import StandardScaler",
   "id": "ab96070ad4ec2144",
   "outputs": [],
   "execution_count": 95
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:48:05.103360Z",
     "start_time": "2025-04-10T09:48:05.092183Z"
    }
   },
   "cell_type": "code",
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaler = scaler.fit_transform(X, y)\n",
    "svm_clf_scaled = SVC(kernel='linear', C=float('inf'))\n",
    "svm_clf_scaled.fit(X_scaler, y)"
   ],
   "id": "62f7f9ecb6651a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ],
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 96
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T09:49:00.082556Z",
     "start_time": "2025-04-10T09:48:59.862410Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(14, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "plot_decision_boundary(svm_clf_unscaled, 0, 6)\n",
    "plt.axis((0, 6, 0, 90))\n",
    "plt.xlabel('$x_0$', fontsize=14)\n",
    "plt.ylabel('$x_1$', fontsize=14)\n",
    "plt.title('Unscaled', fontsize=16)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(X_scaler[:, 0], X_scaler[:, 1], c=y)\n",
    "plot_decision_boundary(svm_clf_scaled, -2, 2)\n",
    "plt.axis((-2, 2, -2, 2))\n",
    "plt.xlabel('$x_0$', fontsize=14)\n",
    "plt.title('Scaled', fontsize=16)"
   ],
   "id": "133a8e37082f90be",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scaled')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 99
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 软间隔\n",
    "\n",
    "![](./SVM/3.png)"
   ],
   "id": "e8b12e333e330f65"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T10:35:38.675636Z",
     "start_time": "2025-04-10T10:35:38.670852Z"
    }
   },
   "cell_type": "code",
   "source": "from sklearn.svm import LinearSVC",
   "id": "4d08892f4d47cb0b",
   "outputs": [],
   "execution_count": 163
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T10:37:26.908483Z",
     "start_time": "2025-04-10T10:37:26.902852Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = iris.data[:, (2, 3)]\n",
    "y = (iris.target == 2).astype(np.float64)\n",
    "scaler = StandardScaler()\n",
    "X_scaler = scaler.fit_transform(X, y)"
   ],
   "id": "b022ac7e6b099811",
   "outputs": [],
   "execution_count": 169
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T10:37:55.468622Z",
     "start_time": "2025-04-10T10:37:55.459809Z"
    }
   },
   "cell_type": "code",
   "source": [
    "svm_clf1 = LinearSVC(C=1, random_state=42)\n",
    "svm_clf1.fit(X_scaler, y)"
   ],
   "id": "a893c30c6db96ca9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1, random_state=42)"
      ],
      "text/html": [
       "<style>#sk-container-id-30 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-30 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-30 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-30 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-30 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-30 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-30 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-30 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-30 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-30 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-30 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-30 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-30 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-30 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-30 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-30 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" checked><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=1, random_state=42)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 170
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T10:37:56.049151Z",
     "start_time": "2025-04-10T10:37:56.040187Z"
    }
   },
   "cell_type": "code",
   "source": [
    "svm_clf2 = LinearSVC(C=100, random_state=42)\n",
    "svm_clf2.fit(X_scaler, y)"
   ],
   "id": "8538e50a5eefc0e0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=100, random_state=42)"
      ],
      "text/html": [
       "<style>#sk-container-id-31 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-31 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-31 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-31 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-31 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-31 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-31 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-31 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-31 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-31 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-31 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-31 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-31 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-31 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-31 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-31 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-31\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=100, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" checked><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=100, random_state=42)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 171
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:04:25.002121Z",
     "start_time": "2025-04-10T12:04:24.819211Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(14, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(X_scaler[:, 0], X_scaler[:, 1], c=y)\n",
    "plot_decision_boundary(svm_clf1, X_scaler.max(), X_scaler.min(), False)\n",
    "plt.axis((-0.5, 2, -0.5, 2,))\n",
    "plt.xlabel('Petal length', fontsize=14)\n",
    "plt.ylabel('Petal width', fontsize=14)\n",
    "plt.title(f'$C = {svm_clf1.C}$', fontsize=16)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(X_scaler[:, 0], X_scaler[:, 1], c=y)\n",
    "plot_decision_boundary(svm_clf2, X_scaler.max(), X_scaler.min(), False)\n",
    "plt.axis((-0.5, 2, -0.5, 2,))\n",
    "plt.xlabel('Petal length', fontsize=14)\n",
    "plt.title(f'$C = {svm_clf2.C}$', fontsize=16)"
   ],
   "id": "f4046e20313aeba2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$C = 100$')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 175
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "- 在左侧，使用较低的 C 值，间隔要大得多，但很多实例最终会出现在间隔之内。\n",
    "- 在右侧，使用较高的 C 值，分类器会减少误分类，但最终会有较小间隔。"
   ],
   "id": "a0cc38804d3de025"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 非线性支持向量机",
   "id": "11d7050ce1b355ac"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:05:50.441148Z",
     "start_time": "2025-04-10T12:05:50.341384Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axis):\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "    plt.axis(axis)\n",
    "    # 在图表中同时显示主刻度和次刻度位置的网格线\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel('$x_1$', fontsize=16)\n",
    "    plt.ylabel('$x_2$', fontsize=16, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, (-1.5, 2.5, -1, 1.5))"
   ],
   "id": "db8ea905ae1682ce",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 176
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:09:12.751438Z",
     "start_time": "2025-04-10T12:09:12.746560Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ],
   "id": "c485c7baf87fd793",
   "outputs": [],
   "execution_count": 178
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:21:19.843426Z",
     "start_time": "2025-04-10T12:21:19.828963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "poly_svm_clf = Pipeline([\n",
    "    ('PolynomialFeatures', PolynomialFeatures(degree=3)), \n",
    "    ('StandardScaler', StandardScaler()), \n",
    "    ('LinearSVC', LinearSVC(C=10)), \n",
    "])\n",
    "poly_svm_clf.fit(X, y)"
   ],
   "id": "f4bb90753794312c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('PolynomialFeatures', PolynomialFeatures(degree=3)),\n",
       "                ('StandardScaler', StandardScaler()),\n",
       "                ('LinearSVC', LinearSVC(C=10))])"
      ],
      "text/html": [
       "<style>#sk-container-id-45 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-45 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-45 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-45 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-45 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-45 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-45 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-45 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-45 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-45 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-45 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-45 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-45 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-45 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-45 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-45 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-45 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-45 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-45 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-45\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;PolynomialFeatures&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;StandardScaler&#x27;, StandardScaler()),\n",
       "                (&#x27;LinearSVC&#x27;, LinearSVC(C=10))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;PolynomialFeatures&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;StandardScaler&#x27;, StandardScaler()),\n",
       "                (&#x27;LinearSVC&#x27;, LinearSVC(C=10))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PolynomialFeatures</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=10)</pre></div> </div></div></div></div></div></div>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 212
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:21:20.279237Z",
     "start_time": "2025-04-10T12:21:20.273594Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_predictions(model, axis):\n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], 100), \n",
    "        np.linspace(axis[0], axis[1], 100)\n",
    "    )\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = model.predict(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)"
   ],
   "id": "b358fee4562763e8",
   "outputs": [],
   "execution_count": 213
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:21:21.134787Z",
     "start_time": "2025-04-10T12:21:21.037615Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plot_predictions(poly_svm_clf, (-1.5, 2.5, -1, 1.5))\n",
    "plot_dataset(X, y, (-1.5, 2.5, -1, 1.5))"
   ],
   "id": "e1035c870d53eff0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 214
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## SVM 中的核函数",
   "id": "aef58e4b501eff94"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:40:17.794684Z",
     "start_time": "2025-04-10T12:40:17.786360Z"
    }
   },
   "cell_type": "code",
   "source": [
    "poly_kernel_svm_clf = Pipeline([\n",
    "    ('StandardScaler', StandardScaler()), \n",
    "    ('SVC', SVC(kernel='poly', degree=3, coef0=1, C=5)),    # coef0 偏置项\n",
    "])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ],
   "id": "201bb6dbe5026906",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('StandardScaler', StandardScaler()),\n",
       "                ('SVC', SVC(C=5, coef0=1, kernel='poly'))])"
      ],
      "text/html": [
       "<style>#sk-container-id-65 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-65 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-65 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-65 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-65 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-65 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-65 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-65 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-65 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-65 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-65 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-65 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-65 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-65 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-65 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-65 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-65 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-65 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-65 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-65\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;StandardScaler&#x27;, StandardScaler()),\n",
       "                (&#x27;SVC&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-183\" type=\"checkbox\" ><label for=\"sk-estimator-id-183\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;StandardScaler&#x27;, StandardScaler()),\n",
       "                (&#x27;SVC&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-184\" type=\"checkbox\" ><label for=\"sk-estimator-id-184\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-185\" type=\"checkbox\" ><label for=\"sk-estimator-id-185\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div></div></div>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 249
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:40:18.364003Z",
     "start_time": "2025-04-10T12:40:18.343855Z"
    }
   },
   "cell_type": "code",
   "source": [
    "poly10_kernel_svm_clf = Pipeline([\n",
    "    ('StandardScaler', StandardScaler()), \n",
    "    ('SVC', SVC(kernel='poly', degree=10, coef0=100, C=5)), \n",
    "])\n",
    "poly10_kernel_svm_clf.fit(X, y)"
   ],
   "id": "c023d3e881c0dbf2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('StandardScaler', StandardScaler()),\n",
       "                ('SVC', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ],
      "text/html": [
       "<style>#sk-container-id-66 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-66 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-66 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-66 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-66 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-66 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-66 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-66 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-66 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-66 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-66 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-66 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-66 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-66 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-66 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-66 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-66 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-66 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-66 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-66\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;StandardScaler&#x27;, StandardScaler()),\n",
       "                (&#x27;SVC&#x27;, SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-186\" type=\"checkbox\" ><label for=\"sk-estimator-id-186\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;StandardScaler&#x27;, StandardScaler()),\n",
       "                (&#x27;SVC&#x27;, SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-187\" type=\"checkbox\" ><label for=\"sk-estimator-id-187\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-188\" type=\"checkbox\" ><label for=\"sk-estimator-id-188\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div></div></div>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 250
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T12:40:27.635021Z",
     "start_time": "2025-04-10T12:40:27.395698Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(14, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, (-1.5, 2.5, -1, 1.5))\n",
    "plot_dataset(X, y, (-1.5, 2.5, -1, 1.5))\n",
    "plt.title('$d = 3, co = 1, C = 5$', fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly10_kernel_svm_clf, (-1.5, 2.5, -1, 1.5))\n",
    "plot_dataset(X, y, (-1.5, 2.5, -1, 1.5))\n",
    "plt.title('$d = 10, co = 100, C = 5$', fontsize=18)"
   ],
   "id": "d11a50f478a688bf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$d = 10, co = 100, C = 5$')"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 252
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 高斯核函数\n",
    "\n",
    "- 利用相似度来变换特征\n",
    "\n",
    "![](./SVM/5.png)\n",
    "\n",
    "![](./SVM/6.png)"
   ],
   "id": "c6e16ae07244d0e6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### SVM 中利用了核函数的计算技巧，大大降低了计算复杂度：\n",
    "\n",
    "- 增加 gamma γ 使高斯曲线变窄，因此每个实例的影响范围都较小，决策边界最终变得更不规则，在个别实例周围摆动。\n",
    "- 减少 gamma γ 使高斯曲线变宽，因此实例具有更大的影响范围，并且决策边界更加平滑。\n",
    "\n",
    "![](./SVM/1.png)"
   ],
   "id": "dcb4af71aa79dbd5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T13:17:12.304427Z",
     "start_time": "2025-04-10T13:17:11.635267Z"
    }
   },
   "cell_type": "code",
   "source": [
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "combos = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in combos:\n",
    "    rbf_svm_clf = Pipeline([\n",
    "        ('StandardScaler', StandardScaler()), \n",
    "        ('SVC', SVC(kernel='rbf', gamma=gamma, C=C)), \n",
    "    ])\n",
    "    rbf_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(18, 12))\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, (-1.5, 2.5, -1, 1.5))\n",
    "    plot_dataset(X, y, (-1.5, 2.5, -1, 1.5))\n",
    "    gamma, C = combos[i]\n",
    "    plt.title(f'$\\gamma = {gamma}, C = {C}$', fontsize=16)"
   ],
   "id": "f7a0564046b883a8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 4 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 269
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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